TxBird-InitAnalysis
Carly Muletz Wolz
12/21/2020
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Read in data
This now is your clean data with your meta data file only having
## Load packages
library(phyloseq)
library(ape)
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.5-6library(ggplot2)
##Set working directory to bring in files##
setwd("/Users/Carly/Dropbox (Smithsonian)/SI_projects/TX_migBirds/Analyses/")
# this is site by species matrix, need row.names = 1 to have phyloseq read it
# there was an issue with original featureTab. Left over _ in sample names and - between controls instead of _. HAd to manually edit to get file in
featureTab <- read.csv("TxBird_feature_tableCLEAN.csv", header = T, row.names = 1)
# make compatible for phyloseq format
featureTab = otu_table(featureTab, taxa_are_rows = TRUE)
## 529 taxa by 18 samples
dim(featureTab)## [1] 522 18# Read taxonomy info in, make matrix and compatible for phyloseq
## This file will be subset to only include the taxonomy that makes to the features (OTUs, ASVs)
taxonomy <- tax_table(as.matrix(read.csv("TxBird_taxonomy.csv", row.names = 1)))
# Read metadata info in, make compatible for phyloseq
## NOTE: we calculated alpha diversity estimates and added to _meta file and called it now _alpha.
## You may need to change row.names = if your sampleID is in a different column. Ours is in column 2
meta_data <- sample_data(read.csv("TxBird_meta_alpha.csv", header = T, row.names = 2))
# Read in sequence data, may need if you want to look at or subset the DNA sequences at some point
library(Biostrings)## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind,
## colMeans, colnames, colSums, dirname, do.call, duplicated,
## eval, evalq, Filter, Find, get, grep, grepl, intersect,
## is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
## paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
## Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
## table, tapply, union, unique, unsplit, which, which.max,
## which.min## Loading required package: S4Vectors## Loading required package: stats4##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:base':
##
## expand.grid## Loading required package: IRanges##
## Attaching package: 'IRanges'## The following object is masked from 'package:phyloseq':
##
## distance## Loading required package: XVector##
## Attaching package: 'Biostrings'## The following object is masked from 'package:ape':
##
## complement## The following object is masked from 'package:base':
##
## strsplitseqs <- readDNAStringSet("TxBird_feature_DNAsequence.fasta")
# You can also add a phylogenetic tree here, if you have one
# library(ape)
# tree = read.tree("FinalRFiles/exported-tree/SalAMPtree.nwk")
# Merge it all together
sample_names(meta_data)## [1] "TX202145335" "TX240130218" "TX240130255" "TX252145317" "TX263105336"
## [6] "TX263105978" "TX272136690" "TX272136698" "TX272136699" "TX279103530"
## [11] "TX279103554" "TX279103561" "TX279103567" "TX279103583" "TX279103586"
## [16] "TX281118809" "TX281118812" "TX281118835"Tx <- merge_phyloseq(featureTab, taxonomy, meta_data, seqs) #tree)
Tx## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 522 taxa and 18 samples ]
## sample_data() Sample Data: [ 18 samples by 21 sample variables ]
## tax_table() Taxonomy Table: [ 522 taxa by 7 taxonomic ranks ]
## refseq() DNAStringSet: [ 522 reference sequences ]sample_names(Tx)## [1] "TX202145335" "TX240130218" "TX240130255" "TX252145317" "TX263105336"
## [6] "TX263105978" "TX272136690" "TX272136698" "TX272136699" "TX279103530"
## [11] "TX279103554" "TX279103561" "TX279103567" "TX279103583" "TX279103586"
## [16] "TX281118809" "TX281118812" "TX281118835"## Total of 175,377 sequences across 18 samples
sum(sample_sums(Tx))## [1] 175377sort(sample_sums(Tx))## TX279103567 TX279103530 TX279103561 TX240130218 TX281118812 TX202145335
## 984 1068 1453 2260 3643 4471
## TX252145317 TX263105978 TX263105336 TX279103586 TX240130255 TX281118809
## 4636 5037 5342 6698 7820 9721
## TX279103583 TX279103554 TX272136699 TX272136690 TX281118835 TX272136698
## 11365 13214 13952 16125 23946 43642## BUT, remember we have to analyze the rarefied dataset because of sequencing coverage issues
Tx = rarefy_even_depth(Tx, replace=FALSE, rngseed = 711)## `set.seed(711)` was used to initialize repeatable random subsampling.## Please record this for your records so others can reproduce.## Try `set.seed(711); .Random.seed` for the full vector## ...## 22OTUs were removed because they are no longer
## present in any sample after random subsampling## ...## In the end, in the results section I would report we had 17,712 high quality sequences we analyzed after normalization to even sequencing depth.
sum(sample_sums(Tx))## [1] 17712### MAKE dataframe of metadata for alpha and beta analyses. This makes sure your metadata are in same order as your feature table
dfTx <- as(sample_data(Tx), "data.frame")
## We also will be interested in just the cloacal swab data for some comparisons and also just the Oven bird (also samples swaisons)
## Let's make these files now for later
TxC <- subset_samples(Tx, Type == "CloacalSwab")
dfTxC <- as(sample_data(TxC), "data.frame")
TxO <- subset_samples(Tx, Species == "OVEN")
dfTxO <- as(sample_data(TxO), "data.frame")Alpha diversity
## Alpha
str(dfTx)## 'data.frame': 18 obs. of 21 variables:
## $ SampleID : Factor w/ 18 levels "TX202145335",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ BandNumber : int 202145335 240130218 240130255 252145317 263105336 263105978 272136690 272136698 272136699 279103530 ...
## $ CollectionDate : Factor w/ 14 levels "4/19/16","4/19/19",..: 14 1 3 10 7 5 8 9 9 2 ...
## $ Species : Factor w/ 2 levels "OVEN","SWTH": 1 1 1 1 1 1 1 1 1 2 ...
## $ Type : Factor w/ 2 levels "CloacalSwab",..: 2 2 2 2 2 2 1 1 1 1 ...
## $ Duplicate : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 2 2 1 ...
## $ Prep : Factor w/ 1 level "Same": 1 1 1 1 1 1 1 1 1 1 ...
## $ Sample_or_Control: Factor w/ 1 level "True Sample": 1 1 1 1 1 1 1 1 1 1 ...
## $ quant_reading : num 10 9 9.3 10 9 10 0.297 21.5 0.577 0.058 ...
## $ is.neg : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ S.obs : int 127 98 69 83 100 162 69 108 117 36 ...
## $ S.chao1 : int 127 98 69 83 100 162 69 108 117 36 ...
## $ se.chao1.x : num 0 0 0 0 0 ...
## $ S.ACE : num 127 98 69 83 100 ...
## $ se.ACE.x : num 5.2 4.64 3.96 4.31 4.54 ...
## $ sample_sums.Tx5. : int 4471 2260 7820 4636 5342 5037 16125 43642 13952 1068 ...
## $ S.obs_rare : int 115 96 57 75 89 147 63 45 112 36 ...
## $ S.chao1_rare : num 129.9 97.4 59.9 79.6 96.6 ...
## $ se.chao1.y : num 8.21 1.74 2.58 3.82 4.98 ...
## $ S.ACE_rare : num 125.7 97.6 62.5 80.5 98.8 ...
## $ se.ACE.y : num 4.25 4.45 3.23 3.74 4.03 ...## let's look at alpha estimates between cloacal swabs and fecal samples
## Remember we are using the rarefied estimates since we had so much variation in sequencing coverage
ggplot(data = dfTx, aes(x=Type, y=S.obs_rare))+geom_boxplot()+theme_bw()+theme_classic()+
theme(text = element_text(size = 20)) + ylab("Bacterial ASV richness") + xlab("")
## plot made with base R, quicker to write, but not as great at modifying for pub as ggplot figures
plot(dfTx$S.obs_rare ~ dfTx$Type)
## how about alpha across samples
plot(dfTx$S.obs_rare ~ dfTx$SampleID)
## make with ggplot instead
ggplot(data = dfTx, aes(x=as.factor(SampleID), y=S.obs_rare))+geom_bar(stat = "identity")+theme_bw()+theme_classic()+
theme(text = element_text(size = 20)) + ylab("Bacterial ASV richness") + xlab("") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
## How about between bird species, but we need to subset to only cloacal swabs for this comparison. So we use the cloacal swab data we subsetted above
## Fecals were only taken for Oven birds
ggplot(data = dfTxC, aes(x=Species, y=S.obs_rare))+geom_boxplot()+theme_bw()+theme_classic()+
theme(text = element_text(size = 20)) + ylab("Bacterial ASV richness") + xlab("")
## Oh, so if oven birds were the only bird with both fecals and clocal swabs then we should subset to them when look at differencees between fecals and cloacals.
ggplot(data = dfTxO, aes(x=Type, y=S.obs_rare))+geom_boxplot()+theme_bw()+theme_classic()+
theme(text = element_text(size = 20)) + ylab("Bacterial ASV richness") + xlab("")
### STATS for between cloacal and fecals and STATs for between bird species
#### CLOACAL vs FECES in Oven birds
## look at distribution of species richness values
## looks normal
hist(dfTxO$S.obs_rare)
## p value is above 0.05, so data is normally distributed
shapiro.test(dfTxO$S.obs_rare)##
## Shapiro-Wilk normality test
##
## data: dfTxO$S.obs_rare
## W = 0.9507, p-value = 0.6472## You can try log10(dfTx$S.obs_rare)
## You can try srqt like this to make data normal, either of those usually work for me
library(car)## Loading required package: carData## testing for homogeneity of variance
## p value is above 0.05, so data similar variances
leveneTest(S.obs_rare~Type, data=dfTxO)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.398 0.5423
## 10## Good to go for t-test and met assumptions
modeAlphaO <- t.test(S.obs_rare~Type, data=dfTxO)
modeAlphaO##
## Welch Two Sample t-test
##
## data: S.obs_rare by Type
## t = -0.77174, df = 8.5446, p-value = 0.4611
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -72.51278 35.84611
## sample estimates:
## mean in group CloacalSwab mean in group Fecal
## 78.16667 96.50000## So, says this is not different in alpha between cloacal and fecals
ggplot(data = dfTxO, aes(x=Type, y=S.obs_rare))+geom_boxplot()+theme_bw()+theme_classic()+
theme(text = element_text(size = 20)) + ylab("Bacterial ASV richness") + xlab("")
## Now for reasons of showing how to do stats with 2+ levels (ANOVA), let's make a third level to compare
str(dfTx)## 'data.frame': 18 obs. of 21 variables:
## $ SampleID : Factor w/ 18 levels "TX202145335",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ BandNumber : int 202145335 240130218 240130255 252145317 263105336 263105978 272136690 272136698 272136699 279103530 ...
## $ CollectionDate : Factor w/ 14 levels "4/19/16","4/19/19",..: 14 1 3 10 7 5 8 9 9 2 ...
## $ Species : Factor w/ 2 levels "OVEN","SWTH": 1 1 1 1 1 1 1 1 1 2 ...
## $ Type : Factor w/ 2 levels "CloacalSwab",..: 2 2 2 2 2 2 1 1 1 1 ...
## $ Duplicate : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 2 2 1 ...
## $ Prep : Factor w/ 1 level "Same": 1 1 1 1 1 1 1 1 1 1 ...
## $ Sample_or_Control: Factor w/ 1 level "True Sample": 1 1 1 1 1 1 1 1 1 1 ...
## $ quant_reading : num 10 9 9.3 10 9 10 0.297 21.5 0.577 0.058 ...
## $ is.neg : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ S.obs : int 127 98 69 83 100 162 69 108 117 36 ...
## $ S.chao1 : int 127 98 69 83 100 162 69 108 117 36 ...
## $ se.chao1.x : num 0 0 0 0 0 ...
## $ S.ACE : num 127 98 69 83 100 ...
## $ se.ACE.x : num 5.2 4.64 3.96 4.31 4.54 ...
## $ sample_sums.Tx5. : int 4471 2260 7820 4636 5342 5037 16125 43642 13952 1068 ...
## $ S.obs_rare : int 115 96 57 75 89 147 63 45 112 36 ...
## $ S.chao1_rare : num 129.9 97.4 59.9 79.6 96.6 ...
## $ se.chao1.y : num 8.21 1.74 2.58 3.82 4.98 ...
## $ S.ACE_rare : num 125.7 97.6 62.5 80.5 98.8 ...
## $ se.ACE.y : num 4.25 4.45 3.23 3.74 4.03 ...## We have species and type factors we can combine to make another set of levels
dfTx$SpeciesType <- as.factor(paste(dfTx$Species, dfTx$Type))
levels(dfTx$SpeciesType)## [1] "OVEN CloacalSwab" "OVEN Fecal" "SWTH CloacalSwab"## Can add this to phyloseq object also
sample_data(Tx)$SpeciesType <- as.factor(paste(sample_data(Tx)$Species, sample_data(Tx)$Type))
shapiro.test(dfTx$S.obs_rare)##
## Shapiro-Wilk normality test
##
## data: dfTx$S.obs_rare
## W = 0.91158, p-value = 0.09175leveneTest(S.obs_rare~SpeciesType, data=dfTx)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 0.7209 0.5024
## 15## good for ANOVA
modeAlpha2 <- aov(S.obs_rare~SpeciesType, data=dfTx)
summary(modeAlpha2)## Df Sum Sq Mean Sq F value Pr(>F)
## SpeciesType 2 5862 2931 2.341 0.13
## Residuals 15 18776 1252## So, says this is not different in alpha between cloacal and fecals
ggplot(data = dfTx, aes(x=SpeciesType, y=S.obs_rare))+geom_boxplot()+theme_bw()+theme_classic()+
theme(text = element_text(size = 20)) + ylab("Bacterial ASV richness") + xlab("")
## But, if sig then run Tukey's, can see swth cloacal almost differnt to oven fecal, which we can see in the plot
TukeyHSD(modeAlpha2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = S.obs_rare ~ SpeciesType, data = dfTx)
##
## $SpeciesType
## diff lwr upr p adj
## OVEN Fecal-OVEN CloacalSwab 18.33333 -34.72382 71.390485 0.6500414
## SWTH CloacalSwab-OVEN CloacalSwab -25.66667 -78.72382 27.390485 0.4399985
## SWTH CloacalSwab-OVEN Fecal -44.00000 -97.05715 9.057152 0.1123501#### BETWEEN SPECIES STATS for alpha, remember need just the cloacal data for this
## not normal, so transform, normal with log10 transformation, use this in stats then
shapiro.test(dfTxC$S.obs_rare)##
## Shapiro-Wilk normality test
##
## data: dfTxC$S.obs_rare
## W = 0.81489, p-value = 0.01391shapiro.test(log10(dfTxC$S.obs_rare))##
## Shapiro-Wilk normality test
##
## data: log10(dfTxC$S.obs_rare)
## W = 0.93273, p-value = 0.41leveneTest(log10(S.obs_rare)~Species, data=dfTxC)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.4805 0.504
## 10## Good to go for t-test and met assumptions with log10
modeAlphaC <- t.test(log10(S.obs_rare)~Species, data=dfTxC)
modeAlphaC##
## Welch Two Sample t-test
##
## data: log10(S.obs_rare) by Species
## t = 1.0574, df = 8.4339, p-value = 0.3197
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1532966 0.4172659
## sample estimates:
## mean in group OVEN mean in group SWTH
## 1.826989 1.695005## So not different, can visualize again
ggplot(data = dfTxC, aes(x=Species, y=S.obs_rare))+geom_boxplot()+theme_bw()+theme_classic()+
theme(text = element_text(size = 20)) + ylab("Bacterial ASV richness") + xlab("")
##Can visualize also on log10 to see what stats see
ggplot(data = dfTxC, aes(x=Species, y=log10(S.obs_rare)))+geom_boxplot()+theme_bw()+theme_classic()+
theme(text = element_text(size = 20)) + ylab("Bacterial ASV richness") + xlab("")
## You can then go through with other alpha diversity estimates as desired
## I do not suggest analyzing Shannon as it voliates test assumptions since it is generally on scale to 1-7 and not truly numeric, need to analyze with special statistics
## I prefer species richness and faith's phylogenetic diversity. Evenness may also be of interest too. But, don't throw the kitchen sink at it. Think about why you are analyzing an alpha metric and what it would mean biologically before you start analyzingBeta diversity
### NOTE: When you do jaccard, it is critical that you put binary = T, so that it is looking at presence absence data.
jacc_tx <- phyloseq::distance(Tx, "jaccard", binary = T)
jacc.ord <- ordinate(Tx, method = "PCoA", jacc_tx)
## Look at our three levels we made earlier, species type
p_jacc <- plot_ordination(Tx, jacc.ord, color = "Type", shape = "Species")
p_jacc + geom_point(size = 4) +theme_bw() +theme_classic()+
theme(text = element_text(size = 20))+ stat_ellipse(aes(group = SpeciesType))
## Probably better to plot like this
p_jacc2 <- plot_ordination(Tx, jacc.ord, color = "SpeciesType")
p_jacc2 + geom_point(size = 4) +theme_bw() +theme_classic()+
theme(text = element_text(size = 20))+ stat_ellipse(aes(group = SpeciesType))
### STATS
jacc_adonisST <- adonis(jacc_tx ~ SpeciesType, data = dfTx)
jacc_adonisST##
## Call:
## adonis(formula = jacc_tx ~ SpeciesType, data = dfTx)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## SpeciesType 2 1.5520 0.77599 2.1837 0.2255 0.001 ***
## Residuals 15 5.3303 0.35535 0.7745
## Total 17 6.8823 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(pairwiseAdonis)## Loading required package: cluster## So, looks like a type effect we are picking up here, fecals are different
pairwise.adonis(jacc_tx, dfTx$SpeciesType)## pairs Df SumsOfSqs F.Model R2
## 1 OVEN Fecal vs OVEN CloacalSwab 1 0.8575601 2.421652 0.1949541
## 2 OVEN Fecal vs SWTH CloacalSwab 1 1.0255301 2.994420 0.2304389
## 3 OVEN CloacalSwab vs SWTH CloacalSwab 1 0.4448820 1.204153 0.1074738
## p.value p.adjusted sig
## 1 0.002 0.006 *
## 2 0.004 0.012 .
## 3 0.200 0.600### Let's see if there is a species effect we are missing because of the huge effect of fecal vs cloaca
jacc_sp <- phyloseq::distance(TxC, "jaccard", binary = T)
jacc.ordS <- ordinate(TxC, method = "PCoA", jacc_sp)
p_jaccS <- plot_ordination(TxC, jacc.ordS, color = "Species", shape = "Species")
p_jaccS + geom_point(size = 4) +theme_bw() +theme_classic()+
theme(text = element_text(size = 20))+ stat_ellipse(aes(group = Species))
### STATS, no species effect, low sample size though
jacc_adonisSp <- adonis(jacc_sp ~ Species, data = dfTxC)
jacc_adonisSp##
## Call:
## adonis(formula = jacc_sp ~ Species, data = dfTxC)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Species 1 0.4449 0.44488 1.2042 0.10747 0.239
## Residuals 10 3.6946 0.36946 0.89253
## Total 11 4.1394 1.00000## Ok, last bit, how about between oven cloacal and oven fecal
jacc_t <- phyloseq::distance(TxO, "jaccard", binary = T)
jacc.ordO <- ordinate(TxO, method = "PCoA", jacc_t)
p_jaccT <- plot_ordination(TxO, jacc.ordO, color = "Type", shape = "Type")
p_jaccT + geom_point(size = 4) +theme_bw() +theme_classic()+
theme(text = element_text(size = 20))+ stat_ellipse(aes(group = Type))
### STATS, no species effect, low sample size though
jacc_adonisT <- adonis(jacc_t ~ Type, data = dfTxO)
jacc_adonisT##
## Call:
## adonis(formula = jacc_t ~ Type, data = dfTxO)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Type 1 0.8576 0.85756 2.4217 0.19495 0.004 **
## Residuals 10 3.5412 0.35412 0.80505
## Total 11 4.3988 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## But, I feel like this would be the plot I would show, and I would do the stats separately. Species effect with just cloacal samples, and type effect of just oven birds
## Dress the figure up a bit
p_jacc2 <- plot_ordination(Tx, jacc.ord, color = "SpeciesType", shape = "SpeciesType")
p_jacc2 + geom_point(size = 4) +theme_bw() +theme_classic()+
theme(text = element_text(size = 20))+ stat_ellipse(aes(group = SpeciesType)) +
labs("Sample type") + scale_color_manual(values = c("blue", "chocolate4", "green"), name = "Sample type") +
scale_shape_manual(values = c(17, 19, 15), name = "Sample type")
## Can repeat these analyses with Bray-Curtis, etc. I prefer Jaccard and Bray-Curtis as defaults. One looking at presence-absence differces (Jaccard) and one considered relative abundances and differences (Bray-Curtis). Unifrac and weigthed unifrac as useful too i fyou have a phylogenetic tree.Relative abundance and taxonomy distribution
## taxonomy table of just cloacal swabs, was interested in this for some reason
## leaving code in case of interest
tax <- as(tax_table(TxC), "matrix")
#write.csv(tax, "TxBird_taxonomyCloacaAfterFiltering.csv")
rank_names(Tx)## [1] "Kingdom" "Phylum" "Class" "Order" "Family" "Genus" "Species"## Distribution across samples of some genus of bacterial pathogens of concerns.
## Each bar means it is a different ASV
Tx_diplor = subset_taxa(Tx, Genus == "Diplorickettsia")
plot_bar(Tx_diplor)## Warning in psmelt(physeq): The sample variables:
## Species
## have been renamed to:
## sample_Species
## to avoid conflicts with taxonomic rank names.
Tx_myco = subset_taxa(Tx, Genus == "Mycobacterium")
plot_bar(Tx_myco)## Warning in psmelt(physeq): The sample variables:
## Species
## have been renamed to:
## sample_Species
## to avoid conflicts with taxonomic rank names.
## Using pipes for this code
## Much better than creating lots of objects like old code I had
library(magrittr)
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:Biostrings':
##
## collapse, intersect, setdiff, setequal, union## The following object is masked from 'package:XVector':
##
## slice## The following objects are masked from 'package:IRanges':
##
## collapse, desc, intersect, setdiff, slice, union## The following objects are masked from 'package:S4Vectors':
##
## first, intersect, rename, setdiff, setequal, union## The following objects are masked from 'package:BiocGenerics':
##
## combine, intersect, setdiff, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionTx_phylum <- Tx %>%
tax_glom(taxrank = "Phylum") %>% # agglomerate at phylum level
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() %>% # Melt to long format
arrange(Phylum) # Sort data frame alphabetically ## Warning in psmelt(.): The sample variables:
## Species
## have been renamed to:
## sample_Species
## to avoid conflicts with taxonomic rank names.## get warning message, no worries
p_RA <- ggplot(data=Tx_phylum, aes(x=Sample, y=Abundance, fill=Phylum))
p_RA + geom_bar(aes(), stat="identity", position="stack") +
ylab("Relative abundance (% of sequences)") +theme_classic() + theme_bw() +
theme_classic()+ labs(x = "")+ theme(text = element_text(size = 18))
## better to make your own colors
cbPalette <- c("black", "mediumpurple", "#999999", "#CC79A7", "#56B4E9", "#009E73", "aquamarine2", "#F0E442","#0072B2","#E69F00","#D55E00" , "gray44", "darkolivegreen1")
p_RA + geom_bar(aes(), stat="identity", position="stack") +
ylab("Relative abundance (% of sequences)") +theme_classic() + theme_bw() +
theme_classic()+ labs(x = "")+ theme(text = element_text(size = 18)) + scale_fill_manual(values=cbPalette,name="Phylum")
## Let's merge by SpeciesType
Tx_phylum <- Tx %>%
tax_glom(taxrank = "Phylum") %>% # agglomerate at phylum level
merge_samples("SpeciesType") %>% # merge samples on variable of interest
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() %>% # Melt to long format
arrange(Phylum) ## Warning in psmelt(.): The sample variables:
## Species
## have been renamed to:
## sample_Species
## to avoid conflicts with taxonomic rank names.ggplot(data=Tx_phylum, aes(x=Sample, y=Abundance, fill=Phylum)) +geom_bar(aes(), stat="identity", position="stack") +
ylab("Relative abundance (% of sequences)") +theme_classic() + theme_bw() +
theme_classic()+ labs(x = "")+ theme(text = element_text(size = 18)) + scale_fill_manual(values=cbPalette,name="Phylum") + theme(axis.text.x = element_text(angle = 50, vjust = 0.5))
## How about at genus level?
## NOTE: we need to filter low abundance ASVs
## This will take a little time to run depending on dataset
## NOTE: when you get to these lower tax levels you need to think about tax_glom and the NArm = T or F option. See the help file of tax_glom. I feel like you shouldn't remove taxa that don't have a genus assignment, so I prefer NArm = F when doing analyses, but for making the plots NArm = T (default) is preferred or otherwise challenging to plot. Still thinking through this, but a note that taxa are removed that don't have genus level assignments when you do tax_glom at genus level
Tx_genus05 <- Tx %>%
tax_glom(taxrank = "Genus", NArm = T) %>% # agglomerate at phylum level
merge_samples("SpeciesType") %>% # merge samples on variable of interest
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() %>% # Melt to long format
filter(Abundance > 0.05) %>%
arrange(Genus) ## Warning in psmelt(.): The sample variables:
## Species
## have been renamed to:
## sample_Species
## to avoid conflicts with taxonomic rank names.## Can play around with the filter number. Could try 1% or 5%, etc
ggplot(data=Tx_genus05, aes(x=Sample, y=Abundance, fill=Genus)) +geom_bar(aes(), stat="identity", position="stack") +
ylab("Relative abundance (% of sequences)") +theme_classic() + theme_bw() +
theme_classic()+ labs(x = "")+ theme(text = element_text(size = 18)) + scale_fill_manual(values=cbPalette,name="Genus") + theme(axis.text.x = element_text(angle = 50, vjust = 0.5))
Tx_genus01 <- Tx %>%
tax_glom(taxrank = "Genus") %>% # agglomerate at phylum level
merge_samples("SpeciesType") %>% # merge samples on variable of interest
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() %>% # Melt to long format
filter(Abundance > 0.01) %>%
arrange(Genus) ## Warning in psmelt(.): The sample variables:
## Species
## have been renamed to:
## sample_Species
## to avoid conflicts with taxonomic rank names.## Will let use default colors for now, would need to provide many more colors to get cbPalette to fill in.
ggplot(data=Tx_genus01, aes(x=Sample, y=Abundance, fill=Genus)) +geom_bar(aes(), stat="identity", position="stack") +
ylab("Relative abundance (% of sequences)") +theme_classic() + theme_bw() +
theme_classic()+ labs(x = "")+ theme(text = element_text(size = 18)) + theme(axis.text.x = element_text(angle = 50, vjust = 0.5))
## Remove legend so can see plot
ggplot(data=Tx_genus01, aes(x=Sample, y=Abundance, fill=Genus)) +geom_bar(aes(), stat="identity", position="stack") +
ylab("Relative abundance (% of sequences)") +theme_classic() + theme_bw() +
theme_classic()+ labs(x = "")+ theme(text = element_text(size = 18)) + theme(axis.text.x = element_text(angle = 50, vjust = 0.5)) + theme(legend.position = "none")
## Ok, not totally in love with this figure. Would need some tweaks or annotation in legend
## or this addition might help
Tx_genus2 <- Tx %>%
tax_glom(taxrank = "Genus") %>% # agglomerate at phylum level
merge_samples("SpeciesType") %>% # merge samples on variable of interest
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() %>% # Melt to long format
arrange(Genus) ## Warning in psmelt(.): The sample variables:
## Species
## have been renamed to:
## sample_Species
## to avoid conflicts with taxonomic rank names.Tx_genus2$Genus <- as.character(Tx_genus2$Genus) #convert to character
Tx_genus2$Genus[Tx_genus2$Abundance < 0.05] <- "Genera < 5% abund."
ggplot(data=Tx_genus2, aes(x=Sample, y=Abundance, fill=Genus)) +geom_bar(aes(), stat="identity", position="stack") +
ylab("Relative abundance (% of sequences)") +theme_classic() + theme_bw() +
theme_classic()+ labs(x = "")+ theme(text = element_text(size = 18)) + theme(axis.text.x = element_text(angle = 50, vjust = 0.5)) + scale_fill_manual(values=cbPalette,name="Genus")
Differential abundances
#### DIFFERNTIAL ABUNDANCE TESTING ##########
library(DAtest)## DAtest version 2.7.15## More here: https://github.com/Russel88/DAtest/wiki
## DAtest does not perform well with ASV level.
## Posted on github https://github.com/Russel88/DAtest/issues/9
## Basically can only look at FPR and use that to rank.
## For this example let's look at differences in abundances of ASVs and of phylum between fecal and swabs as we know there are differences there.
## Change min number of samples based on your data. Not a lot of samples here so doing low for 3
TxODA <- preDA(TxO, min.samples = 3, min.reads = 1, min.abundance = 0) ## 368 features grouped as 'Others' in the output## Testing DA for 133 ASVs
TxODA ## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 133 taxa and 12 samples ]
## sample_data() Sample Data: [ 12 samples by 21 sample variables ]
## tax_table() Taxonomy Table: [ 133 taxa by 7 taxonomic ranks ]## Will take some time to run depending on data size
mytest <- testDA(TxODA, predictor = "Type")## Running on 3 cores## predictor is assumed to be a categorical variable with 2 levels: CloacalSwab, Fecal## Spikeing...
## Testing 28 methods 20 times each...
##
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|=================================================================| 100%## Setting levels: control = 0, case = 1## Setting levels: control = 0, case = 1
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## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1
## Setting levels: control = 0, case = 1## The higher the score the better, but our data does not return scores
## read more here: https://github.com/Russel88/DAtest/issues/9
## From the paper - In general, the methods per- form better with paired tests, with higher AUC and lower FPR
summary(mytest)## Warning in summary.DA(mytest): Best Score is equal to zero!
## You might want to re-run with a higher effectSize or pruned dataset (see preDA)## Method AUC FPR FDR Power Score Score.5% Score.95%
## MgSeq Feature (msf) 0.59 0.06 0.00 0.00 0.00 -1.00 0.02
## EdgeR qll - TMM (erq) 0.54 0.11 0.00 0.00 0.00 -1.00 0.01
## DESeq2 (ds2x) 0.55 0.03 0.00 0.00 0.00 -1.00 0.01
## EdgeR qll - RLE (erq2) 0.57 0.12 0.00 0.00 0.00 -1.00 0.00
## LIMMA voom (vli) 0.53 0.07 0.00 0.00 0.00 -1.00 0.00
## ALDEx2 t-test (adx) 0.46 0.00 0.00 0.00 0.00 0.00 0.00
## ALDEx2 wilcox (adx) 0.49 0.00 0.00 0.00 0.00 0.00 0.00
## baySeq (bay) 0.47 0.00 0.00 0.00 0.00 0.00 0.00
## DESeq2 man. geoMeans (ds2) 0.62 0.02 0.00 0.00 0.00 0.00 0.00
## EdgeR exact - RLE (ere2) 0.59 0.03 0.00 0.00 0.00 0.00 0.00
## EdgeR exact - TMM (ere) 0.61 0.03 0.00 0.00 0.00 0.00 0.00
## LIMMA - ALR (lia) 0.61 0.03 0.00 0.00 0.00 0.00 0.00
## LIMMA - CLR (lic) 0.59 0.03 0.00 0.00 0.00 0.00 0.00
## Log LIMMA (lli) 0.52 0.02 0.00 0.00 0.00 0.00 0.00
## Log LIMMA 2 (lli2) 0.54 0.03 0.00 0.00 0.00 0.00 0.00
## Log t-test (ltt) 0.49 0.01 0.00 0.00 0.00 0.00 0.00
## Log t-test2 (ltt2) 0.50 0.01 0.00 0.00 0.00 0.00 0.00
## Permutation (per) 0.45 0.01 0.00 0.00 0.00 0.00 0.00
## Quasi-Poisson GLM (qpo) 0.65 0.00 0.00 0.00 0.00 0.00 0.00
## t-test - ALR (tta) 0.55 0.00 0.00 0.00 0.00 0.00 0.00
## t-test - CLR (ttc) 0.55 0.02 0.00 0.00 0.00 0.00 0.00
## t-test (ttt) 0.55 0.00 0.00 0.00 0.00 0.00 0.00
## Wilcox (wil) 0.50 0.02 0.00 0.00 0.00 0.00 0.00
## Negbinom GLM (neb) 0.73 0.15 0.68 0.40 -0.59 -0.82 -0.29
## ZI-Poisson GLM (zpo) 0.71 0.36 0.81 0.70 -0.66 -0.95 -0.46
## Poisson GLM (poi) 0.71 0.40 0.81 0.70 -0.66 -0.89 -0.46
## ZI-NegBin GLM (znb) 0.68 0.21 0.76 0.43 -0.68 -0.95 -0.43
## MgSeq ZIG (zig) 0.69 0.26 0.77 0.47 -0.68 -0.93 -0.49
## SAMseq (sam) 0.65 NA 0.78 0.10 -0.77 -1.00 -0.66
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## *## For our data looks like lia and ds2 look like two good tests to compare
## I generally pick two tests and compare their results to find sig ASVs
## The tests are all called DA.xx to run in DAtest package. For instance lia is DA.lia
res.lia <- DA.lia(TxODA, predictor = "Type")
res.lia## Feature logFC AveExpr t pval pval.adj
## 1 ASV10 -1.632808844 -5.250044 -2.013112418 0.0533976397 0.39158269
## 2 ASV100 -0.820570091 -5.450627 -1.479626883 0.1496756291 0.58109362
## 3 ASV103 1.446530629 -4.723249 2.670456821 0.0122476735 0.16166929
## 4 ASV105 0.669871331 -4.955218 1.098763042 0.2808437067 0.81638216
## 5 ASV107 -0.902320528 -5.615289 -1.569819387 0.1272253194 0.58109362
## 6 ASV11 -0.413806474 -4.640989 -0.510410640 0.6135987121 0.98212756
## 7 ASV1107 -0.113203213 -5.761590 -0.209854123 0.8352380625 0.98212756
## 8 ASV1133 0.162445585 -5.643142 0.290693868 0.7733390075 0.98212756
## 9 ASV114 1.692543646 -4.858717 3.085001665 0.0044217756 0.09727906
## 10 ASV1155 -0.407043028 -5.908510 -0.785250246 0.4386285491 0.94916342
## 11 ASV1156 -0.065174371 -5.756952 -0.116520511 0.9080372934 0.98212756
## 12 ASV1180 0.319106950 -5.545435 0.561165257 0.5789639401 0.98212756
## 13 ASV12 -2.706972961 -4.712962 -3.493698893 0.0015390960 0.05079017
## 14 ASV121 -0.263075636 -5.171012 -0.411990566 0.6833534838 0.98212756
## 15 ASV123 -0.715869532 -5.490678 -1.304903294 0.2021104721 0.72104277
## 16 ASV1231 0.006560135 -5.701708 0.012468595 0.9901365120 0.99545319
## 17 ASV124 -0.316444897 -5.477254 -0.509246678 0.6144041793 0.98212756
## 18 ASV132 -0.864241303 -5.527674 -1.505485564 0.1429349919 0.58109362
## 19 ASV133 -0.227866733 -5.377346 -0.364589413 0.7180436229 0.98212756
## 20 ASV135 -0.179762349 -5.290202 -0.284814160 0.7777956769 0.98212756
## 21 ASV137 -0.409975173 -5.204583 -0.668773138 0.5088902842 0.98212756
## 22 ASV1470 -0.196878466 -5.813193 -0.377066518 0.7088491730 0.98212756
## 23 ASV15 -0.723989469 -4.910991 -1.019550308 0.3163104574 0.83505961
## 24 ASV151 -0.265950706 -5.319956 -0.436989390 0.6653319475 0.98212756
## 25 ASV158 -0.701557434 -5.506338 -1.222211622 0.2313966684 0.79849104
## 26 ASV159 0.909021523 -5.159962 1.493923153 0.1459181028 0.58109362
## 27 ASV167 -0.116962308 -5.303515 -0.183903764 0.8553593950 0.98212756
## 28 ASV170 0.131710638 -5.484225 0.219582406 0.8277233331 0.98212756
## 29 ASV174 -0.845217864 -5.425090 -1.405198047 0.1705067823 0.64305415
## 30 ASV178 -0.190624034 -5.382575 -0.303204570 0.7638825092 0.98212756
## 31 ASV183 -0.139628767 -5.457372 -0.249511360 0.8047111694 0.98212756
## 32 ASV190 -0.884890153 -5.624004 -1.630351452 0.1137624566 0.58109362
## 33 ASV192 0.959395543 -5.009484 1.604166975 0.1194341849 0.58109362
## 34 ASV193 1.460615076 -4.974681 2.610650998 0.0141138662 0.16936639
## 35 ASV194 -0.305286425 -5.636348 -0.572399528 0.5714300843 0.98212756
## 36 ASV196 0.223474967 -5.371968 0.358768522 0.7223479131 0.98212756
## 37 ASV199 -0.105125400 -5.368408 -0.184716677 0.8547275012 0.98212756
## 38 ASV20 -2.542932717 -4.794982 -3.647476434 0.0010240861 0.04931628
## 39 ASV201 -0.044398385 -5.468714 -0.073923532 0.9415750369 0.98641194
## 40 ASV203 0.062087648 -5.452661 0.107452734 0.9151643158 0.98212756
## 41 ASV209 0.049134180 -5.459138 0.084196708 0.9334743129 0.98641194
## 42 ASV210 0.605507159 -5.296476 1.036862786 0.3083056315 0.83053762
## 43 ASV215 1.096904313 -5.166302 1.765517761 0.0879273751 0.51310614
## 44 ASV219 -0.421874410 -5.479827 -0.725837446 0.4737130879 0.98111923
## 45 ASV22 0.101041291 -4.009873 0.124310970 0.9019203991 0.98212756
## 46 ASV223 0.588245160 -5.429294 1.050249484 0.3022133889 0.83053762
## 47 ASV232 0.114011711 -5.474646 0.196504619 0.8455757183 0.98212756
## 48 ASV238 -0.342136677 -5.673202 -0.615613579 0.5429158604 0.98212756
## 49 ASV241 -1.268674597 -5.444411 -2.091847222 0.0452523382 0.37333179
## 50 ASV242 -0.576135496 -5.680153 -1.048626078 0.3029476755 0.83053762
## 51 ASV245 0.014859829 -5.408698 0.025760640 0.9796234050 0.99545319
## 52 ASV248 0.402072186 -5.503952 0.686427893 0.4978553101 0.98212756
## 53 ASV250 0.191545240 -5.435880 0.337235726 0.7383499766 0.98212756
## 54 ASV253 0.218592389 -5.427833 0.390573243 0.6989460889 0.98212756
## 55 ASV256 -0.049459831 -5.508435 -0.076496568 0.9395454854 0.98641194
## 56 ASV26 -1.615911291 -4.968737 -2.251091770 0.0320698778 0.29631344
## 57 ASV260 0.494793538 -5.457592 0.842489215 0.4063582754 0.94916342
## 58 ASV264 0.279139891 -5.459660 0.467468592 0.6436326415 0.98212756
## 59 ASV267 0.561856262 -5.424060 0.990445757 0.3300885577 0.85434686
## 60 ASV268 0.411289934 -5.326007 0.666793145 0.5101362242 0.98212756
## 61 ASV278 0.714891732 -5.347543 1.180732904 0.2472321625 0.80146213
## 62 ASV28 -1.979534445 -5.076682 -2.774046579 0.0095488685 0.14005007
## 63 ASV281 1.055720711 -5.177128 1.755472651 0.0896597155 0.51310614
## 64 ASV29 -0.105824430 -5.023175 -0.136958564 0.8920029906 0.98212756
## 65 ASV30 -0.817665897 -4.368860 -1.098080771 0.2811365701 0.81638216
## 66 ASV301 0.871115255 -5.269431 1.535682605 0.1353759501 0.58109362
## 67 ASV302 1.136296718 -5.136840 1.941140210 0.0619425195 0.40294284
## 68 ASV303 0.061942189 -5.703160 0.114981328 0.9092465167 0.98212756
## 69 ASV309 0.113762495 -5.648107 0.209795943 0.8352830529 0.98212756
## 70 ASV31 -2.024521878 -5.070175 -2.489689475 0.0187194708 0.19007463
## 71 ASV319 0.224657124 -5.419324 0.399156516 0.6926806218 0.98212756
## 72 ASV326 -0.103490698 -5.588874 -0.194186420 0.8473738069 0.98212756
## 73 ASV331 0.262925606 -5.400189 0.443533969 0.6606468449 0.98212756
## 74 ASV332 0.482188217 -5.473660 0.787217269 0.4374944386 0.94916342
## 75 ASV34 -1.960430866 -5.086233 -2.807240192 0.0088093952 0.14005007
## 76 ASV340 -0.626991894 -5.497927 -1.090288601 0.2844968130 0.81638216
## 77 ASV343 -0.401949671 -5.767246 -0.769231714 0.4479301989 0.95365784
## 78 ASV349 0.345150525 -5.532413 0.608417185 0.5476121461 0.98212756
## 79 ASV361 -0.139657995 -5.706863 -0.251439534 0.8032344559 0.98212756
## 80 ASV375 0.460336085 -5.474820 0.851661700 0.4013288981 0.94916342
## 81 ASV376 -0.298166716 -5.699164 -0.528470454 0.6011645642 0.98212756
## 82 ASV38 -1.183516732 -5.474690 -1.971098995 0.0582505938 0.40294284
## 83 ASV381 -0.412899602 -5.637820 -0.722560893 0.4756941700 0.98111923
## 84 ASV40 -1.754505600 -5.189196 -2.826629843 0.0084026840 0.14005007
## 85 ASV403 0.206122971 -5.601927 0.361475885 0.7203447778 0.98212756
## 86 ASV408 0.003163716 -5.603125 0.005747579 0.9954531864 0.99545319
## 87 ASV43 -0.511436995 -4.853339 -0.708266256 0.4843927376 0.98212756
## 88 ASV44 -0.402645296 -5.037193 -0.610702531 0.5461184675 0.98212756
## 89 ASV451 0.723860265 -5.343058 1.311340694 0.1999553498 0.72104277
## 90 ASV47 0.390114379 -5.096543 0.631673518 0.5325119632 0.98212756
## 91 ASV472 0.271361378 -5.569308 0.490240707 0.6276250744 0.98212756
## 92 ASV480 0.100833866 -5.548813 0.187778689 0.8523482200 0.98212756
## 93 ASV482 0.341278007 -5.534350 0.573080530 0.5709749759 0.98212756
## 94 ASV483 -0.699427751 -5.622928 -1.210159482 0.2359178077 0.79849104
## 95 ASV489 0.168249906 -5.620864 0.296286268 0.7691073912 0.98212756
## 96 ASV53 -1.500768314 -5.316065 -2.228892321 0.0336719819 0.29631344
## 97 ASV54 0.809853040 -4.910178 1.145561324 0.2612736983 0.80204949
## 98 ASV562 0.368912040 -5.520532 0.649940770 0.5208084413 0.98212756
## 99 ASV570 0.096750514 -5.666379 0.178149034 0.8598354307 0.98212756
## 100 ASV591 0.654691451 -5.377643 1.158539916 0.2560261235 0.80204949
## 101 ASV6 -2.784822018 -4.205523 -4.022146919 0.0003727774 0.04920662
## 102 ASV61 -0.125589665 -5.077344 -0.182749715 0.8562566279 0.98212756
## 103 ASV62 -1.981886516 -5.075506 -3.328731789 0.0023687795 0.06253578
## 104 ASV65 1.184301655 -5.112838 1.924310782 0.0641045426 0.40294284
## 105 ASV665 -0.426746438 -5.853076 -0.825230569 0.4159281008 0.94916342
## 106 ASV672 0.200862106 -5.604557 0.335812510 0.7394119442 0.98212756
## 107 ASV68 0.908863666 -4.895292 1.564556556 0.1284550829 0.58109362
## 108 ASV69 0.521933793 -4.829866 0.835584188 0.4101703764 0.94916342
## 109 ASV70 -1.539023942 -5.122705 -2.533959412 0.0168932429 0.18582567
## 110 ASV72 -0.907543380 -5.182505 -1.533109318 0.1360071893 0.58109362
## 111 ASV728 0.497632978 -5.456172 0.862199490 0.3955996631 0.94916342
## 112 ASV74 0.007167104 -5.346783 0.009709984 0.9923186757 0.99545319
## 113 ASV759 -0.499236017 -5.646028 -0.925306344 0.3623867851 0.91990492
## 114 ASV76 -1.215389197 -5.458754 -2.027055837 0.0518674124 0.39158269
## 115 ASV762 0.066430999 -5.681539 0.116465026 0.9080808803 0.98212756
## 116 ASV784 -0.012626868 -5.711302 -0.022419345 0.9822658681 0.99545319
## 117 ASV8 -2.940434377 -4.596232 -3.613562420 0.0011208246 0.04931628
## 118 ASV806 0.487576090 -5.461200 0.820577294 0.4185320962 0.94916342
## 119 ASV808 0.058484198 -5.675746 0.108497698 0.9143426254 0.98212756
## 120 ASV81 -0.109069299 -5.307191 -0.187981738 0.8521904944 0.98212756
## 121 ASV819 0.151859797 -5.629059 0.283179561 0.7790360364 0.98212756
## 122 ASV832 0.168932379 -5.620522 0.312290351 0.7570378738 0.98212756
## 123 ASV835 -0.423364829 -5.700865 -0.792358417 0.4345386444 0.94916342
## 124 ASV850 0.171389426 -5.619294 0.305575338 0.7620946292 0.98212756
## 125 ASV851 -0.075151611 -5.752330 -0.141883233 0.8881461006 0.98212756
## 126 ASV87 0.314119750 -5.121595 0.443421658 0.6607271279 0.98212756
## 127 ASV884 -0.032412228 -5.721195 -0.062350553 0.9507082744 0.98813773
## 128 ASV90 -0.962809064 -5.598258 -1.734932719 0.0932920248 0.51310614
## 129 ASV91 1.013671700 -5.092394 1.494072959 0.1458791346 0.58109362
## 130 ASV925 -0.141814883 -5.785662 -0.270402704 0.7887514798 0.98212756
## 131 ASV959 -0.062116681 -5.736047 -0.112204131 0.9114289153 0.98212756
## 132 ASV97 0.819070840 -5.122117 1.176380615 0.2489389935 0.80146213
## B ordering Method Kingdom
## 1 -4.1792813 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 2 -4.9948198 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 3 -2.9329863 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 4 -5.4455727 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 5 -4.8712542 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 6 -5.8949891 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 7 -5.9998963 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 8 -5.9802249 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 9 -2.0425021 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 10 -5.7241832 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 11 -6.0147241 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 12 -5.8687608 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 13 -1.1071045 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 14 -5.9388832 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 15 -5.2162393 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 16 -6.0212624 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 17 -5.8955620 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 18 -4.9600246 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 19 -5.9567224 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 20 -5.9818689 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 21 -5.8051477 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 22 -5.9522357 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 23 -5.5241345 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 24 -5.9286087 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 25 -5.3124809 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 26 -4.9756462 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 27 -6.0048684 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 28 -5.9978640 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 29 -5.0920855 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 30 -5.9766160 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 31 -5.9910366 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 32 -4.7849069 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 33 -4.8225904 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 34 -3.0555454 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 35 -5.8626269 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 36 -5.9587645 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 37 -6.0047225 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 38 -0.7437481 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 39 -6.0186756 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 40 -6.0157132 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 41 -6.0178843 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 42 -5.5074275 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 43 -4.5825120 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 44 -5.7670416 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 45 -6.0138103 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 46 -5.4943307 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 47 -6.0025357 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 48 -5.8379269 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 49 -4.0427803 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 50 -5.4959272 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 51 -6.0210148 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 52 -5.7936801 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 53 -5.9660353 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 54 -5.9472103 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 55 -6.0184871 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 56 -3.7553400 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 57 -5.6798495 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 58 -5.9152748 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 59 -5.5516325 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 60 -5.8064157 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 61 -5.3586292 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 62 -2.7169438 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 63 -4.5979985 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 64 -6.0122012 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 65 -5.4462724 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 66 -4.9187466 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 67 -4.3006518 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 68 -6.0148976 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 69 -5.9999082 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 70 -3.2982960 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 71 -5.9439259 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 72 -6.0029764 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 73 -5.9258203 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 74 -5.7227090 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 75 -2.6467484 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 76 -5.4542355 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 77 -5.7360565 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 78 -5.8421616 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 79 -5.9905670 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 80 -5.6724694 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 81 -5.8859353 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 82 -4.2505330 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 83 -5.7693110 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 84 -2.6055342 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 85 -5.9578187 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 86 -6.0213221 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 87 -5.7790958 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 88 -5.8408221 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 89 -5.2085129 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 90 -5.8283018 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 91 -5.9047354 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 92 -6.0041674 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 93 -5.8622512 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 94 -5.3260375 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 95 -5.9786304 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 96 -3.7962923 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 97 -5.3966313 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 98 -5.8170612 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 99 -6.0058824 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 100 -5.3827295 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 101 0.1609431 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 102 -6.0050743 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 103 -1.4904587 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 104 -4.3285514 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 105 -5.6935296 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 106 -5.9665001 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 107 -4.8786330 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 108 -5.6853551 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 109 -3.2102714 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 110 -4.9222911 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 111 -5.6638973 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 112 -6.0212922 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 113 -5.6104797 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 114 -4.1553880 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 115 -6.0147304 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 116 -6.0210933 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 117 -0.8243381 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 118 -5.6971720 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 119 -6.0156033 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 120 -6.0041303 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 121 -5.9823200 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 122 -5.9739003 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 123 -5.7188394 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 124 -5.9759151 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 125 -6.0115325 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 126 -5.9258684 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 127 -6.0194440 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 128 -4.6294454 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 129 -4.9754444 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 130 -5.9857569 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 131 -6.0152049 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 132 -5.3633881 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## Phylum Class
## 1 Proteobacteria Gammaproteobacteria
## 2 Proteobacteria Alphaproteobacteria
## 3 Actinobacteria Actinobacteria
## 4 Actinobacteria Actinobacteria
## 5 Firmicutes Bacilli
## 6 Proteobacteria Gammaproteobacteria
## 7 Actinobacteria Actinobacteria
## 8 Acidobacteria Acidobacteria_Gp16
## 9 Actinobacteria Actinobacteria
## 10 Proteobacteria Alphaproteobacteria
## 11 Actinobacteria Actinobacteria
## 12 Actinobacteria Actinobacteria
## 13 Cyanobacteria/Chloroplast Chloroplast
## 14 Actinobacteria Actinobacteria
## 15 Proteobacteria Gammaproteobacteria
## 16 Actinobacteria Actinobacteria
## 17 Actinobacteria Actinobacteria
## 18 Actinobacteria Actinobacteria
## 19 Chloroflexi Thermomicrobia
## 20 Actinobacteria Actinobacteria
## 21 Firmicutes Erysipelotrichia
## 22 Chloroflexi Chloroflexia
## 23 Proteobacteria Gammaproteobacteria
## 24 Actinobacteria Actinobacteria
## 25 Actinobacteria Actinobacteria
## 26 Actinobacteria Actinobacteria
## 27 Firmicutes Bacilli
## 28 Actinobacteria Actinobacteria
## 29 Actinobacteria Actinobacteria
## 30 Actinobacteria Actinobacteria
## 31 Actinobacteria Actinobacteria
## 32 Actinobacteria Actinobacteria
## 33 Actinobacteria Actinobacteria
## 34 Actinobacteria Actinobacteria
## 35 Actinobacteria Actinobacteria
## 36 Firmicutes Erysipelotrichia
## 37 Proteobacteria Alphaproteobacteria
## 38 Proteobacteria Gammaproteobacteria
## 39 Actinobacteria Actinobacteria
## 40 Firmicutes Clostridia
## 41 Actinobacteria Actinobacteria
## 42 Actinobacteria Actinobacteria
## 43 Actinobacteria Actinobacteria
## 44 Proteobacteria Alphaproteobacteria
## 45 Firmicutes Bacilli
## 46 Proteobacteria Alphaproteobacteria
## 47 Actinobacteria Actinobacteria
## 48 Actinobacteria Actinobacteria
## 49 Proteobacteria Alphaproteobacteria
## 50 Proteobacteria Gammaproteobacteria
## 51 Firmicutes Clostridia
## 52 Firmicutes Clostridia
## 53 Firmicutes Clostridia
## 54 Actinobacteria Actinobacteria
## 55 Chloroflexi Thermomicrobia
## 56 Actinobacteria Actinobacteria
## 57 Actinobacteria Actinobacteria
## 58 Firmicutes Clostridia
## 59 Firmicutes Clostridia
## 60 Firmicutes Clostridia
## 61 Actinobacteria Actinobacteria
## 62 Cyanobacteria/Chloroplast Chloroplast
## 63 Actinobacteria Actinobacteria
## 64 Firmicutes Bacilli
## 65 Actinobacteria Actinobacteria
## 66 Proteobacteria Alphaproteobacteria
## 67 Proteobacteria Alphaproteobacteria
## 68 Actinobacteria Actinobacteria
## 69 Actinobacteria Actinobacteria
## 70 Actinobacteria Actinobacteria
## 71 Proteobacteria Alphaproteobacteria
## 72 Firmicutes Erysipelotrichia
## 73 Actinobacteria Actinobacteria
## 74 Bacteroidetes Bacteroidia
## 75 Proteobacteria Gammaproteobacteria
## 76 Actinobacteria Actinobacteria
## 77 Actinobacteria Actinobacteria
## 78 Actinobacteria Actinobacteria
## 79 Actinobacteria Actinobacteria
## 80 Actinobacteria Actinobacteria
## 81 Proteobacteria Betaproteobacteria
## 82 Proteobacteria Gammaproteobacteria
## 83 Firmicutes Clostridia
## 84 Proteobacteria Gammaproteobacteria
## 85 Proteobacteria Alphaproteobacteria
## 86 Actinobacteria Actinobacteria
## 87 Actinobacteria Actinobacteria
## 88 Actinobacteria Actinobacteria
## 89 Proteobacteria Alphaproteobacteria
## 90 Proteobacteria Gammaproteobacteria
## 91 Actinobacteria Actinobacteria
## 92 Firmicutes Clostridia
## 93 Proteobacteria Alphaproteobacteria
## 94 Actinobacteria Actinobacteria
## 95 Proteobacteria Alphaproteobacteria
## 96 Proteobacteria Gammaproteobacteria
## 97 Actinobacteria Actinobacteria
## 98 Proteobacteria Alphaproteobacteria
## 99 Proteobacteria Alphaproteobacteria
## 100 Actinobacteria Actinobacteria
## 101 Actinobacteria Actinobacteria
## 102 Actinobacteria Actinobacteria
## 103 Firmicutes Bacilli
## 104 Cyanobacteria/Chloroplast Chloroplast
## 105 Proteobacteria Alphaproteobacteria
## 106 Chloroflexi <NA>
## 107 Actinobacteria Actinobacteria
## 108 Actinobacteria Actinobacteria
## 109 Actinobacteria Actinobacteria
## 110 Actinobacteria Actinobacteria
## 111 Actinobacteria Actinobacteria
## 112 Actinobacteria Actinobacteria
## 113 Actinobacteria Actinobacteria
## 114 Proteobacteria Gammaproteobacteria
## 115 Actinobacteria Actinobacteria
## 116 Actinobacteria Actinobacteria
## 117 Proteobacteria Gammaproteobacteria
## 118 Verrucomicrobia Spartobacteria
## 119 Actinobacteria Actinobacteria
## 120 Firmicutes Clostridia
## 121 Bacteroidetes Bacteroidia
## 122 Proteobacteria Alphaproteobacteria
## 123 Verrucomicrobia Spartobacteria
## 124 Proteobacteria Alphaproteobacteria
## 125 Proteobacteria Alphaproteobacteria
## 126 Proteobacteria Gammaproteobacteria
## 127 Actinobacteria Actinobacteria
## 128 Cyanobacteria/Chloroplast Chloroplast
## 129 Firmicutes Erysipelotrichia
## 130 Firmicutes Clostridia
## 131 Proteobacteria Alphaproteobacteria
## 132 Firmicutes Clostridia
## Order Family
## 1 Oceanospirillales Halomonadaceae
## 2 Rhizobiales Methylobacteriaceae
## 3 Solirubrobacterales Solirubrobacteraceae
## 4 Actinomycetales Pseudonocardiaceae
## 5 Lactobacillales Carnobacteriaceae
## 6 Enterobacteriales Enterobacteriaceae
## 7 Solirubrobacterales <NA>
## 8 Gp16 <NA>
## 9 Solirubrobacterales Solirubrobacteraceae
## 10 Rhizobiales Xanthobacteraceae
## 11 Solirubrobacterales Solirubrobacteraceae
## 12 Actinomycetales Nocardioidaceae
## 13 Chloroplast Streptophyta
## 14 Actinomycetales Microbacteriaceae
## 15 Enterobacteriales Enterobacteriaceae
## 16 Rubrobacterales Rubrobacteraceae
## 17 Actinomycetales Mycobacteriaceae
## 18 Actinomycetales Microbacteriaceae
## 19 Sphaerobacterales Sphaerobacteraceae
## 20 Solirubrobacterales Solirubrobacteraceae
## 21 Erysipelotrichales Erysipelotrichaceae
## 22 Kallotenuales Kallotenuaceae
## 23 Enterobacteriales Enterobacteriaceae
## 24 Actinomycetales Mycobacteriaceae
## 25 Actinomycetales Nocardioidaceae
## 26 Actinomycetales Microbacteriaceae
## 27 Bacillales Bacillaceae_1
## 28 Actinomycetales Microbacteriaceae
## 29 Actinomycetales Microbacteriaceae
## 30 Actinomycetales Intrasporangiaceae
## 31 Actinomycetales Nocardioidaceae
## 32 Actinomycetales Microbacteriaceae
## 33 Rubrobacterales Rubrobacteraceae
## 34 Rubrobacterales Rubrobacteraceae
## 35 Actinomycetales Mycobacteriaceae
## 36 Erysipelotrichales Erysipelotrichaceae
## 37 Rhizobiales Methylobacteriaceae
## 38 Pseudomonadales Pseudomonadaceae
## 39 Actinomycetales Nocardioidaceae
## 40 Clostridiales Lachnospiraceae
## 41 Actinomycetales Nocardioidaceae
## 42 Solirubrobacterales Solirubrobacteraceae
## 43 Actinomycetales Propionibacteriaceae
## 44 Rhizobiales Methylobacteriaceae
## 45 Lactobacillales Enterococcaceae
## 46 Rhizobiales Bradyrhizobiaceae
## 47 Actinomycetales Nocardioidaceae
## 48 Actinomycetales Nocardioidaceae
## 49 Rhizobiales Methylobacteriaceae
## 50 Enterobacteriales Enterobacteriaceae
## 51 Clostridiales Ruminococcaceae
## 52 Clostridiales Eubacteriaceae
## 53 Clostridiales Lachnospiraceae
## 54 Gaiellales Gaiellaceae
## 55 Sphaerobacterales Sphaerobacteraceae
## 56 Actinomycetales Pseudonocardiaceae
## 57 Actinomycetales Pseudonocardiaceae
## 58 Clostridiales Ruminococcaceae
## 59 Clostridiales Lachnospiraceae
## 60 Clostridiales Lachnospiraceae
## 61 Actinomycetales Propionibacteriaceae
## 62 Chloroplast Streptophyta
## 63 Actinomycetales <NA>
## 64 Lactobacillales Carnobacteriaceae
## 65 Solirubrobacterales Patulibacteraceae
## 66 Alphaproteobacteria_incertae_sedis Geminicoccus
## 67 Rhizobiales Rhodobiaceae
## 68 Actinomycetales Streptomycetaceae
## 69 <NA> <NA>
## 70 Actinomycetales Propionibacteriaceae
## 71 Rhizobiales Methylobacteriaceae
## 72 Erysipelotrichales Erysipelotrichaceae
## 73 Solirubrobacterales <NA>
## 74 Bacteroidales Bacteroidaceae
## 75 Pseudomonadales Pseudomonadaceae
## 76 Actinomycetales Pseudonocardiaceae
## 77 Actinomycetales Mycobacteriaceae
## 78 Actinomycetales Cellulomonadaceae
## 79 Actinomycetales Pseudonocardiaceae
## 80 Solirubrobacterales Conexibacteraceae
## 81 Burkholderiales Alcaligenaceae
## 82 Pseudomonadales Pseudomonadaceae
## 83 Clostridiales Ruminococcaceae
## 84 Pseudomonadales Moraxellaceae
## 85 Rhizobiales Methylobacteriaceae
## 86 Actinomycetales <NA>
## 87 Actinomycetales Pseudonocardiaceae
## 88 Actinomycetales Microbacteriaceae
## 89 Rhizobiales Methylobacteriaceae
## 90 Enterobacteriales Enterobacteriaceae
## 91 Actinomycetales Kineosporiaceae
## 92 Clostridiales Ruminococcaceae
## 93 Alphaproteobacteria_incertae_sedis Geminicoccus
## 94 Actinomycetales Micrococcaceae
## 95 Rhodospirillales Reyranella
## 96 Pseudomonadales Moraxellaceae
## 97 Coriobacteriales Coriobacteriaceae
## 98 Rhizobiales Hyphomicrobiaceae
## 99 Rhizobiales Methylobacteriaceae
## 100 Solirubrobacterales Solirubrobacteraceae
## 101 Actinomycetales Sanguibacteraceae
## 102 Actinomycetales <NA>
## 103 Bacillales Staphylococcaceae
## 104 Chloroplast Chlorophyta
## 105 Sphingomonadales Sphingomonadaceae
## 106 <NA> <NA>
## 107 Actinomycetales Mycobacteriaceae
## 108 Actinomycetales Nocardiaceae
## 109 Actinomycetales Pseudonocardiaceae
## 110 Actinomycetales Pseudonocardiaceae
## 111 Acidimicrobiales Acidimicrobiaceae
## 112 Actinomycetales Propionibacteriaceae
## 113 Actinomycetales Propionibacteriaceae
## 114 Pseudomonadales Pseudomonadaceae
## 115 Acidimicrobiales Acidimicrobiaceae
## 116 Actinomycetales Intrasporangiaceae
## 117 Pseudomonadales Pseudomonadaceae
## 118 Terrimicrobium <NA>
## 119 Gaiellales Gaiellaceae
## 120 Clostridiales Ruminococcaceae
## 121 Bacteroidales Bacteroidaceae
## 122 Alphaproteobacteria_incertae_sedis Geminicoccus
## 123 Terrimicrobium <NA>
## 124 Rhizobiales Hyphomicrobiaceae
## 125 Alphaproteobacteria_incertae_sedis Geminicoccus
## 126 Enterobacteriales Enterobacteriaceae
## 127 Solirubrobacterales <NA>
## 128 Chloroplast Streptophyta
## 129 Erysipelotrichales Erysipelotrichaceae
## 130 Clostridiales Lachnospiraceae
## 131 Alphaproteobacteria_incertae_sedis Geminicoccus
## 132 Clostridiales Lachnospiraceae
## Genus Species
## 1 Salinicola socius
## 2 Methylobacterium <NA>
## 3 Solirubrobacter <NA>
## 4 Actinomycetospora <NA>
## 5 Carnobacterium <NA>
## 6 Buttiauxella <NA>
## 7 <NA> <NA>
## 8 <NA> <NA>
## 9 Solirubrobacter <NA>
## 10 <NA> <NA>
## 11 Solirubrobacter <NA>
## 12 Nocardioides <NA>
## 13 <NA> <NA>
## 14 Agrococcus <NA>
## 15 <NA> <NA>
## 16 Rubrobacter <NA>
## 17 Mycobacterium <NA>
## 18 Curtobacterium <NA>
## 19 <NA> <NA>
## 20 Solirubrobacter <NA>
## 21 Clostridium_XVIII <NA>
## 22 Kallotenue <NA>
## 23 Enterobacter <NA>
## 24 Mycobacterium JC2972
## 25 Nocardioides <NA>
## 26 Microbacterium <NA>
## 27 Bacillus <NA>
## 28 Curtobacterium <NA>
## 29 Microbacterium <NA>
## 30 <NA> <NA>
## 31 Nocardioides <NA>
## 32 Pseudoclavibacter helvolus
## 33 Rubrobacter <NA>
## 34 Rubrobacter <NA>
## 35 Mycobacterium <NA>
## 36 Clostridium_XVIII <NA>
## 37 <NA> <NA>
## 38 Pseudomonas <NA>
## 39 Nocardioides <NA>
## 40 <NA> <NA>
## 41 Nocardioides ginsengagri
## 42 Solirubrobacter <NA>
## 43 Microlunatus <NA>
## 44 <NA> <NA>
## 45 Enterococcus <NA>
## 46 Bradyrhizobium <NA>
## 47 Nocardioides <NA>
## 48 Nocardioides <NA>
## 49 Methylobacterium <NA>
## 50 Pantoea <NA>
## 51 Intestinimonas <NA>
## 52 Anaerofustis <NA>
## 53 <NA> <NA>
## 54 Gaiella <NA>
## 55 Sphaerobacter <NA>
## 56 Actinomycetospora <NA>
## 57 Pseudonocardia <NA>
## 58 <NA> <NA>
## 59 Clostridium_XlVa <NA>
## 60 <NA> <NA>
## 61 Microlunatus <NA>
## 62 <NA> <NA>
## 63 <NA> <NA>
## 64 Catellicoccus <NA>
## 65 Patulibacter minatonensis
## 66 <NA> <NA>
## 67 <NA> <NA>
## 68 Streptomyces <NA>
## 69 <NA> <NA>
## 70 Friedmanniella <NA>
## 71 Microvirga zambiensis
## 72 Clostridium_XVIII <NA>
## 73 <NA> <NA>
## 74 Bacteroides <NA>
## 75 Pseudomonas <NA>
## 76 Pseudonocardia <NA>
## 77 Mycobacterium <NA>
## 78 Cellulomonas <NA>
## 79 Pseudonocardia <NA>
## 80 Conexibacter <NA>
## 81 Achromobacter <NA>
## 82 Pseudomonas <NA>
## 83 Anaerofilum <NA>
## 84 Acinetobacter <NA>
## 85 Microvirga <NA>
## 86 <NA> <NA>
## 87 Pseudonocardia endophytica
## 88 Microbacterium <NA>
## 89 Microvirga <NA>
## 90 Pantoea <NA>
## 91 Pseudokineococcus <NA>
## 92 Clostridium_IV <NA>
## 93 <NA> <NA>
## 94 Arthrobacter <NA>
## 95 <NA> <NA>
## 96 Acinetobacter <NA>
## 97 Denitrobacterium <NA>
## 98 Rhodoplanes <NA>
## 99 Microvirga lotononidis
## 100 Solirubrobacter <NA>
## 101 Sanguibacter <NA>
## 102 <NA> <NA>
## 103 Staphylococcus <NA>
## 104 <NA> <NA>
## 105 Sphingopyxis <NA>
## 106 <NA> <NA>
## 107 Mycobacterium <NA>
## 108 Rhodococcus <NA>
## 109 Actinomycetospora chiangmaiensis
## 110 Actinomycetospora <NA>
## 111 Ilumatobacter <NA>
## 112 <NA> <NA>
## 113 Friedmanniella <NA>
## 114 Pseudomonas <NA>
## 115 Ilumatobacter <NA>
## 116 Janibacter <NA>
## 117 Pseudomonas <NA>
## 118 <NA> <NA>
## 119 Gaiella <NA>
## 120 Intestinimonas <NA>
## 121 Bacteroides <NA>
## 122 <NA> <NA>
## 123 <NA> <NA>
## 124 Pedomicrobium <NA>
## 125 <NA> <NA>
## 126 Pantoea <NA>
## 127 <NA> <NA>
## 128 <NA> <NA>
## 129 Clostridium_XVIII <NA>
## 130 Clostridium_XlVa <NA>
## 131 <NA> <NA>
## 132 <NA> <NA>res.lia[res.lia$pval.adj < 0.05,"Feature"]## [1] "ASV20" "ASV6" "ASV8"## Three ASVs sig: ASV20, ASV6 and ASV8
res.ds2 <- DA.ds2(TxODA, predictor = "Type")## converting counts to integer mode## using pre-existing size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingres.ds2## Feature baseMean log2FoldChange lfcSE stat pval
## 1 ASV10 34.2179534 -23.375001965 2.729266 -8.564574290 NA
## 2 ASV100 1.9243463 -1.980434888 2.207519 -0.897131439 0.3696487845
## 3 ASV103 5.3539615 2.987449711 1.783761 1.674803793 0.0939727344
## 4 ASV105 3.4747901 2.078751667 1.812523 1.146883186 0.2514299027
## 5 ASV107 1.2037348 -3.623599875 2.304323 -1.572522290 0.1158294740
## 6 ASV11 43.3719283 3.225699429 2.231007 1.445848809 NA
## 7 ASV1107 0.5888269 2.721708335 2.372118 1.147374691 0.2512267965
## 8 ASV1133 0.9780784 2.513092593 2.559302 0.981944548 0.3261271674
## 9 ASV114 4.4221562 5.626583871 1.780849 3.159495246 0.0015804268
## 10 ASV1155 0.3186708 1.681392613 2.766583 0.607750565 0.5433529208
## 11 ASV1156 0.6269299 1.626876030 2.625114 0.619735308 0.5354320658
## 12 ASV1180 1.1251168 3.638620415 2.474303 1.470564091 0.1414090411
## 13 ASV12 26.6414817 -8.139802299 2.362738 -3.445072208 0.0005709069
## 14 ASV121 2.8460418 0.103859064 2.199885 0.047211127 0.9623449597
## 15 ASV123 1.7101913 -1.861123788 2.587129 -0.719378128 0.4719079692
## 16 ASV1231 0.6533466 2.841119957 2.549672 1.114308078 0.2651470426
## 17 ASV124 1.8182171 0.500798337 2.605439 0.192212651 0.8475756362
## 18 ASV132 1.3810911 -2.712049689 2.556477 -1.060854124 0.2887562009
## 19 ASV133 3.0391495 -0.634819789 2.644490 -0.240053729 0.8102886043
## 20 ASV135 1.9416786 0.414403210 2.352979 0.176118540 0.8602008157
## 21 ASV137 2.7023639 0.553801245 2.200846 0.251631075 0.8013262367
## 22 ASV1470 0.4763546 1.479419090 2.662416 0.555667830 0.5784379527
## 23 ASV15 9.6832666 -1.704645528 2.341745 -0.727938054 NA
## 24 ASV151 2.0130301 0.005002347 2.024228 0.002471237 0.9980282404
## 25 ASV158 1.5948986 -2.245101421 2.385886 -0.940992554 0.3467086751
## 26 ASV159 3.2284607 3.331136513 1.978387 1.683763589 0.0922273666
## 27 ASV167 1.7389911 0.853275387 2.150129 0.396848497 0.6914791858
## 28 ASV170 1.7127238 1.682631416 2.201323 0.764372882 0.4446450646
## 29 ASV174 1.3916588 -1.301793449 2.002727 -0.650010497 0.5156854414
## 30 ASV178 3.0491960 -0.141002407 2.456425 -0.057401477 0.9542253868
## 31 ASV183 2.0528914 -0.571352108 2.228422 -0.256393128 0.7976472936
## 32 ASV190 0.7991581 -3.094147115 2.524835 -1.225485061 0.2203925153
## 33 ASV192 3.3183006 3.403896121 1.923860 1.769305148 0.0768429648
## 34 ASV193 3.9222150 5.453097509 1.816361 3.002210501 0.0026802677
## 35 ASV194 0.9664851 -0.112895232 2.609940 -0.043255880 0.9654975607
## 36 ASV196 2.5883061 2.054750821 2.591894 0.792760363 0.4279174542
## 37 ASV199 1.6547881 0.791563281 1.851247 0.427583935 0.6689540642
## 38 ASV20 8.5750594 -6.511677066 1.893844 -3.438338673 0.0005852952
## 39 ASV201 1.3682115 0.311485651 2.575321 0.120950244 0.9037304454
## 40 ASV203 1.5341570 1.054688490 2.571619 0.410126304 0.6817132983
## 41 ASV209 1.4051792 0.875257668 2.359278 0.370985402 0.7106484043
## 42 ASV210 2.9070939 3.718627978 2.333867 1.593333524 0.1110853899
## 43 ASV215 3.3187126 4.652758793 2.078267 2.238769077 0.0251709453
## 44 ASV219 1.5896668 -0.308274939 2.390172 -0.128976028 0.8973766169
## 45 ASV22 19.4604625 0.581493874 1.867965 0.311298116 0.7555739992
## 46 ASV223 1.6052772 3.579071628 1.929883 1.854553840 0.0636599650
## 47 ASV232 1.6335508 1.706242848 2.572862 0.663169224 0.5072221795
## 48 ASV238 0.7267150 -0.487203838 2.631183 -0.185165329 0.8530993621
## 49 ASV241 1.1264070 -3.011513357 2.278219 -1.321871522 0.1862109368
## 50 ASV242 0.8063944 -1.623582462 2.608530 -0.622412844 0.5336704369
## 51 ASV245 2.4705398 1.250423625 2.416253 0.517505191 0.6048035465
## 52 ASV248 1.5780633 4.141046986 2.474776 1.673301925 0.0942678736
## 53 ASV250 2.2779902 2.312057181 2.386021 0.969001268 0.3325445566
## 54 ASV253 1.2458464 2.179071917 2.131742 1.022202417 0.3066851069
## 55 ASV256 2.7020863 2.130374762 2.640018 0.806954703 NA
## 56 ASV26 11.3001751 -4.551338105 2.273280 -2.002102161 0.0452737451
## 57 ASV260 1.3269234 3.897495370 2.448681 1.591671080 0.1114586297
## 58 ASV264 3.2166297 3.858418769 2.594046 1.487413238 0.1369057026
## 59 ASV267 1.8742603 4.388044337 2.455007 1.787385804 0.0738751544
## 60 ASV268 2.0094891 2.116561325 2.337842 0.905348400 0.3652808430
## 61 ASV278 2.9175376 5.022850854 2.467478 2.035621215 0.0417884058
## 62 ASV28 7.9703221 -6.403146675 2.502961 -2.558228707 0.0105206874
## 63 ASV281 3.4419600 5.266180705 2.223621 2.368290763 0.0178704853
## 64 ASV29 8.5655036 -0.597561943 2.740627 -0.218038428 0.8273991706
## 65 ASV30 11.0742620 -1.667642893 1.669526 -0.998872028 0.3178566880
## 66 ASV301 2.7029854 4.906762407 1.892216 2.593130457 0.0095106687
## 67 ASV302 3.0536784 5.093220720 1.851928 2.750226007 0.0059554173
## 68 ASV303 1.4661180 2.414194495 2.256361 1.069950356 0.2846416552
## 69 ASV309 1.0173908 3.481714163 2.304549 1.510800856 0.1308391966
## 70 ASV31 10.4574322 -6.306721966 2.224485 -2.835137439 0.0045805975
## 71 ASV319 1.5601456 1.677207881 2.160225 0.776404198 0.4375103668
## 72 ASV326 0.9499856 1.690625278 2.581445 0.654914294 0.5125229369
## 73 ASV331 1.5678448 1.659280248 2.339299 0.709306696 0.4781341743
## 74 ASV332 2.4373628 4.180800746 2.531835 1.651292625 0.0986788375
## 75 ASV34 6.1251341 -6.021980085 2.473523 -2.434576550 0.0149092259
## 76 ASV340 1.0854008 -0.559391354 2.576636 -0.217101389 0.8281293318
## 77 ASV343 0.9424039 -1.695069609 2.419218 -0.700668325 0.4835100280
## 78 ASV349 1.1652047 3.705260749 2.472000 1.498891745 0.1339017182
## 79 ASV361 1.1725349 -0.293915504 2.435883 -0.120660744 0.9039597532
## 80 ASV375 1.7562221 4.283577118 2.264518 1.891606748 0.0585433962
## 81 ASV376 0.5877378 -0.767587920 2.671627 -0.287311053 0.7738741601
## 82 ASV38 1.9176015 -4.351674104 2.486534 -1.750096394 0.0801016819
## 83 ASV381 0.7693847 -0.667126378 2.625505 -0.254094453 0.7994225883
## 84 ASV40 4.3783658 -5.534606661 2.269305 -2.438899652 0.0147320585
## 85 ASV403 0.8812682 3.311839918 2.289147 1.446756962 0.1479650046
## 86 ASV408 0.8888009 2.049448880 2.343074 0.874683649 0.3817460592
## 87 ASV43 6.2137549 -0.886919627 1.956411 -0.453340029 0.6503039131
## 88 ASV44 3.3394319 0.303105717 2.024614 0.149710382 0.8809931166
## 89 ASV451 1.9552970 4.439169701 2.037761 2.178454040 0.0293722504
## 90 ASV47 4.2840602 1.947045772 2.059235 0.945518797 0.3443940788
## 91 ASV472 1.0590712 3.569590417 2.485346 1.436254632 0.1509298985
## 92 ASV480 1.2892998 2.357206900 2.554772 0.922668132 0.3561801783
## 93 ASV482 1.3074387 3.885923703 2.275922 1.707406365 0.0877465481
## 94 ASV483 0.7241087 -1.428966935 2.629868 -0.543360655 0.5868815098
## 95 ASV489 1.0685228 3.572844392 2.507164 1.425053982 0.1541415974
## 96 ASV53 2.4411203 -4.715794202 2.452644 -1.922738774 0.0545128647
## 97 ASV54 15.1370435 3.355011246 2.535507 1.323210951 0.1857652354
## 98 ASV562 1.4104736 3.979752680 2.474764 1.608133927 0.1078058502
## 99 ASV570 1.1214608 2.960702564 2.551152 1.160535706 0.2458307652
## 100 ASV591 1.5948867 4.157289295 2.217518 1.874748860 0.0608272817
## 101 ASV6 19.4001034 -3.432976187 1.915173 -1.792514659 0.0730505632
## 102 ASV61 7.3848880 -0.114002993 2.534717 -0.044976614 0.9641259495
## 103 ASV62 4.6964277 -5.641844783 2.063417 -2.734224483 0.0062527399
## 104 ASV65 6.9687195 6.278158640 2.126917 2.951764556 NA
## 105 ASV665 0.2464120 -1.541006122 2.828192 -0.544873164 0.5858407542
## 106 ASV672 0.9700731 3.454209411 2.504077 1.379434387 0.1677608632
## 107 ASV68 5.2525932 1.142204714 1.789392 0.638320123 0.5232653151
## 108 ASV69 5.4043550 0.696302532 1.783815 0.390344511 0.6962818132
## 109 ASV70 2.7059538 -3.284091792 2.116851 -1.551404667 0.1208047375
## 110 ASV72 4.0169363 -2.090461325 2.063151 -1.013237366 0.3109467966
## 111 ASV728 1.4884839 4.067325575 2.249586 1.808032571 0.0706014314
## 112 ASV74 8.2967377 2.218387851 2.584731 0.858266366 NA
## 113 ASV759 0.6433571 -0.244263542 2.628376 -0.092933267 0.9259565770
## 114 ASV76 1.4942066 -3.989159326 2.260899 -1.764412903 0.0776624874
## 115 ASV762 0.6224476 2.023497994 2.581300 0.783906586 0.4330949250
## 116 ASV784 0.5686328 2.690566453 2.576496 1.044273673 0.2963587899
## 117 ASV8 21.7998701 -7.853093621 2.298720 -3.416290287 0.0006348054
## 118 ASV806 1.3008334 3.870455733 2.449936 1.579819268 0.1141482619
## 119 ASV808 0.9912038 3.461890811 2.527194 1.369855812 0.1707319157
## 120 ASV81 3.4929768 -0.379204006 2.452329 -0.154630151 0.8771128988
## 121 ASV819 1.0190138 3.509355035 2.509767 1.398279271 0.1620292203
## 122 ASV832 0.7463130 3.070216536 2.295965 1.337222548 0.1811500040
## 123 ASV835 0.5664146 0.249003648 2.663081 0.093502078 0.9255046989
## 124 ASV850 0.6881852 2.960140502 2.518902 1.175171095 0.2399262718
## 125 ASV851 0.5498898 1.743764888 2.617285 0.666249482 0.5052516496
## 126 ASV87 6.1096627 3.029116449 2.434907 1.244037807 0.2134856489
## 127 ASV884 0.6413848 2.831783559 2.564283 1.104318046 0.2694551973
## 128 ASV90 0.6913735 -2.289599735 2.562871 -0.893372951 0.3716574830
## 129 ASV91 6.8622206 5.032678927 2.375147 2.118891359 0.0340996494
## 130 ASV925 0.6260716 1.951085798 2.616211 0.745767656 0.4558077815
## 131 ASV959 0.5479861 2.641157375 2.370463 1.114194935 0.2651955678
## 132 ASV97 6.5294084 4.004002124 2.407971 1.662811391 0.0963501957
## 133 Others 622.4605134 -0.295635066 1.237784 -0.238842256 0.8112279027
## ordering pval.adj Method Kingdom
## 1 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 2 CloacalSwab>Fecal 0.62105921 DESeq2 man. geoMeans (ds2) Bacteria
## 3 Fecal>CloacalSwab 0.36859448 DESeq2 man. geoMeans (ds2) Bacteria
## 4 Fecal>CloacalSwab 0.54121352 DESeq2 man. geoMeans (ds2) Bacteria
## 5 CloacalSwab>Fecal 0.37718829 DESeq2 man. geoMeans (ds2) Bacteria
## 6 Fecal>CloacalSwab NA DESeq2 man. geoMeans (ds2) Bacteria
## 7 Fecal>CloacalSwab 0.54121352 DESeq2 man. geoMeans (ds2) Bacteria
## 8 Fecal>CloacalSwab 0.60026305 DESeq2 man. geoMeans (ds2) Bacteria
## 9 Fecal>CloacalSwab 0.05017855 DESeq2 man. geoMeans (ds2) Bacteria
## 10 Fecal>CloacalSwab 0.74199807 DESeq2 man. geoMeans (ds2) Bacteria
## 11 Fecal>CloacalSwab 0.73912905 DESeq2 man. geoMeans (ds2) Bacteria
## 12 Fecal>CloacalSwab 0.40815791 DESeq2 man. geoMeans (ds2) Bacteria
## 13 CloacalSwab>Fecal 0.02687343 DESeq2 man. geoMeans (ds2) Bacteria
## 14 Fecal>CloacalSwab 0.97316024 DESeq2 man. geoMeans (ds2) Bacteria
## 15 CloacalSwab>Fecal 0.72207605 DESeq2 man. geoMeans (ds2) Bacteria
## 16 Fecal>CloacalSwab 0.55194855 DESeq2 man. geoMeans (ds2) Bacteria
## 17 Fecal>CloacalSwab 0.94996090 DESeq2 man. geoMeans (ds2) Bacteria
## 18 CloacalSwab>Fecal 0.57300059 DESeq2 man. geoMeans (ds2) Bacteria
## 19 CloacalSwab>Fecal 0.93659949 DESeq2 man. geoMeans (ds2) Bacteria
## 20 Fecal>CloacalSwab 0.94996090 DESeq2 man. geoMeans (ds2) Bacteria
## 21 Fecal>CloacalSwab 0.93659949 DESeq2 man. geoMeans (ds2) Bacteria
## 22 Fecal>CloacalSwab 0.77639533 DESeq2 man. geoMeans (ds2) Bacteria
## 23 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 24 Fecal>CloacalSwab 0.99802824 DESeq2 man. geoMeans (ds2) Bacteria
## 25 CloacalSwab>Fecal 0.61155558 DESeq2 man. geoMeans (ds2) Bacteria
## 26 Fecal>CloacalSwab 0.36859448 DESeq2 man. geoMeans (ds2) Bacteria
## 27 Fecal>CloacalSwab 0.86693912 DESeq2 man. geoMeans (ds2) Bacteria
## 28 Fecal>CloacalSwab 0.69715955 DESeq2 man. geoMeans (ds2) Bacteria
## 29 CloacalSwab>Fecal 0.73586574 DESeq2 man. geoMeans (ds2) Bacteria
## 30 CloacalSwab>Fecal 0.97316024 DESeq2 man. geoMeans (ds2) Bacteria
## 31 CloacalSwab>Fecal 0.93659949 DESeq2 man. geoMeans (ds2) Bacteria
## 32 CloacalSwab>Fecal 0.50890635 DESeq2 man. geoMeans (ds2) Bacteria
## 33 Fecal>CloacalSwab 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 34 Fecal>CloacalSwab 0.06807880 DESeq2 man. geoMeans (ds2) Bacteria
## 35 CloacalSwab>Fecal 0.97316024 DESeq2 man. geoMeans (ds2) Bacteria
## 36 Fecal>CloacalSwab 0.69454771 DESeq2 man. geoMeans (ds2) Bacteria
## 37 Fecal>CloacalSwab 0.85815319 DESeq2 man. geoMeans (ds2) Bacteria
## 38 CloacalSwab>Fecal 0.02687343 DESeq2 man. geoMeans (ds2) Bacteria
## 39 Fecal>CloacalSwab 0.95669074 DESeq2 man. geoMeans (ds2) Bacteria
## 40 Fecal>CloacalSwab 0.86577589 DESeq2 man. geoMeans (ds2) Bacteria
## 41 Fecal>CloacalSwab 0.87623638 DESeq2 man. geoMeans (ds2) Bacteria
## 42 Fecal>CloacalSwab 0.37718829 DESeq2 man. geoMeans (ds2) Bacteria
## 43 Fecal>CloacalSwab 0.22833643 DESeq2 man. geoMeans (ds2) Bacteria
## 44 CloacalSwab>Fecal 0.95669074 DESeq2 man. geoMeans (ds2) Bacteria
## 45 Fecal>CloacalSwab 0.92267210 DESeq2 man. geoMeans (ds2) Bacteria
## 46 Fecal>CloacalSwab 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 47 Fecal>CloacalSwab 0.73586574 DESeq2 man. geoMeans (ds2) Bacteria
## 48 CloacalSwab>Fecal 0.94996090 DESeq2 man. geoMeans (ds2) Bacteria
## 49 CloacalSwab>Fecal 0.44620357 DESeq2 man. geoMeans (ds2) Bacteria
## 50 CloacalSwab>Fecal 0.73912905 DESeq2 man. geoMeans (ds2) Bacteria
## 51 Fecal>CloacalSwab 0.79185619 DESeq2 man. geoMeans (ds2) Bacteria
## 52 Fecal>CloacalSwab 0.36859448 DESeq2 man. geoMeans (ds2) Bacteria
## 53 Fecal>CloacalSwab 0.60333084 DESeq2 man. geoMeans (ds2) Bacteria
## 54 Fecal>CloacalSwab 0.58940661 DESeq2 man. geoMeans (ds2) Bacteria
## 55 Fecal>CloacalSwab NA DESeq2 man. geoMeans (ds2) Bacteria
## 56 CloacalSwab>Fecal 0.31943142 DESeq2 man. geoMeans (ds2) Bacteria
## 57 Fecal>CloacalSwab 0.37718829 DESeq2 man. geoMeans (ds2) Bacteria
## 58 Fecal>CloacalSwab 0.40434940 DESeq2 man. geoMeans (ds2) Bacteria
## 59 Fecal>CloacalSwab 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 60 Fecal>CloacalSwab 0.62105921 DESeq2 man. geoMeans (ds2) Bacteria
## 61 Fecal>CloacalSwab 0.31218397 DESeq2 man. geoMeans (ds2) Bacteria
## 62 CloacalSwab>Fecal 0.13361273 DESeq2 man. geoMeans (ds2) Bacteria
## 63 Fecal>CloacalSwab 0.17458090 DESeq2 man. geoMeans (ds2) Bacteria
## 64 CloacalSwab>Fecal 0.93903951 DESeq2 man. geoMeans (ds2) Bacteria
## 65 CloacalSwab>Fecal 0.59364411 DESeq2 man. geoMeans (ds2) Bacteria
## 66 Fecal>CloacalSwab 0.13361273 DESeq2 man. geoMeans (ds2) Bacteria
## 67 Fecal>CloacalSwab 0.09926225 DESeq2 man. geoMeans (ds2) Bacteria
## 68 Fecal>CloacalSwab 0.57300059 DESeq2 man. geoMeans (ds2) Bacteria
## 69 Fecal>CloacalSwab 0.40434940 DESeq2 man. geoMeans (ds2) Bacteria
## 70 CloacalSwab>Fecal 0.09695598 DESeq2 man. geoMeans (ds2) Bacteria
## 71 Fecal>CloacalSwab 0.69454771 DESeq2 man. geoMeans (ds2) Bacteria
## 72 Fecal>CloacalSwab 0.73586574 DESeq2 man. geoMeans (ds2) Bacteria
## 73 Fecal>CloacalSwab 0.72242087 DESeq2 man. geoMeans (ds2) Bacteria
## 74 Fecal>CloacalSwab 0.36859448 DESeq2 man. geoMeans (ds2) Bacteria
## 75 CloacalSwab>Fecal 0.15778931 DESeq2 man. geoMeans (ds2) Bacteria
## 76 CloacalSwab>Fecal 0.93903951 DESeq2 man. geoMeans (ds2) Bacteria
## 77 CloacalSwab>Fecal 0.72242087 DESeq2 man. geoMeans (ds2) Bacteria
## 78 Fecal>CloacalSwab 0.40434940 DESeq2 man. geoMeans (ds2) Bacteria
## 79 CloacalSwab>Fecal 0.95669074 DESeq2 man. geoMeans (ds2) Bacteria
## 80 Fecal>CloacalSwab 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 81 CloacalSwab>Fecal 0.93601922 DESeq2 man. geoMeans (ds2) Bacteria
## 82 CloacalSwab>Fecal 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 83 CloacalSwab>Fecal 0.93659949 DESeq2 man. geoMeans (ds2) Bacteria
## 84 CloacalSwab>Fecal 0.15778931 DESeq2 man. geoMeans (ds2) Bacteria
## 85 Fecal>CloacalSwab 0.41651027 DESeq2 man. geoMeans (ds2) Bacteria
## 86 Fecal>CloacalSwab 0.62963311 DESeq2 man. geoMeans (ds2) Bacteria
## 87 CloacalSwab>Fecal 0.84274079 DESeq2 man. geoMeans (ds2) Bacteria
## 88 Fecal>CloacalSwab 0.95629167 DESeq2 man. geoMeans (ds2) Bacteria
## 89 Fecal>CloacalSwab 0.24868505 DESeq2 man. geoMeans (ds2) Bacteria
## 90 Fecal>CloacalSwab 0.61155558 DESeq2 man. geoMeans (ds2) Bacteria
## 91 Fecal>CloacalSwab 0.41651027 DESeq2 man. geoMeans (ds2) Bacteria
## 92 Fecal>CloacalSwab 0.61965593 DESeq2 man. geoMeans (ds2) Bacteria
## 93 Fecal>CloacalSwab 0.36859448 DESeq2 man. geoMeans (ds2) Bacteria
## 94 CloacalSwab>Fecal 0.77639533 DESeq2 man. geoMeans (ds2) Bacteria
## 95 Fecal>CloacalSwab 0.41651027 DESeq2 man. geoMeans (ds2) Bacteria
## 96 CloacalSwab>Fecal 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 97 Fecal>CloacalSwab 0.44620357 DESeq2 man. geoMeans (ds2) Bacteria
## 98 Fecal>CloacalSwab 0.37718829 DESeq2 man. geoMeans (ds2) Bacteria
## 99 Fecal>CloacalSwab 0.54121352 DESeq2 man. geoMeans (ds2) Bacteria
## 100 Fecal>CloacalSwab 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 101 CloacalSwab>Fecal 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 102 CloacalSwab>Fecal 0.97316024 DESeq2 man. geoMeans (ds2) Bacteria
## 103 CloacalSwab>Fecal 0.09926225 DESeq2 man. geoMeans (ds2) Bacteria
## 104 Fecal>CloacalSwab NA DESeq2 man. geoMeans (ds2) Bacteria
## 105 CloacalSwab>Fecal 0.77639533 DESeq2 man. geoMeans (ds2) Bacteria
## 106 Fecal>CloacalSwab 0.43365907 DESeq2 man. geoMeans (ds2) Bacteria
## 107 Fecal>CloacalSwab 0.73838550 DESeq2 man. geoMeans (ds2) Bacteria
## 108 Fecal>CloacalSwab 0.86693912 DESeq2 man. geoMeans (ds2) Bacteria
## 109 CloacalSwab>Fecal 0.38355504 DESeq2 man. geoMeans (ds2) Bacteria
## 110 CloacalSwab>Fecal 0.58940661 DESeq2 man. geoMeans (ds2) Bacteria
## 111 Fecal>CloacalSwab 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 112 Fecal>CloacalSwab NA DESeq2 man. geoMeans (ds2) Bacteria
## 113 CloacalSwab>Fecal 0.96390562 DESeq2 man. geoMeans (ds2) Bacteria
## 114 CloacalSwab>Fecal 0.36331834 DESeq2 man. geoMeans (ds2) Bacteria
## 115 Fecal>CloacalSwab 0.69454771 DESeq2 man. geoMeans (ds2) Bacteria
## 116 Fecal>CloacalSwab 0.57903948 DESeq2 man. geoMeans (ds2) Bacteria
## 117 CloacalSwab>Fecal 0.02687343 DESeq2 man. geoMeans (ds2) Bacteria
## 118 Fecal>CloacalSwab 0.37718829 DESeq2 man. geoMeans (ds2) Bacteria
## 119 Fecal>CloacalSwab 0.43365907 DESeq2 man. geoMeans (ds2) Bacteria
## 120 CloacalSwab>Fecal 0.95629167 DESeq2 man. geoMeans (ds2) Bacteria
## 121 Fecal>CloacalSwab 0.42870231 DESeq2 man. geoMeans (ds2) Bacteria
## 122 Fecal>CloacalSwab 0.44620357 DESeq2 man. geoMeans (ds2) Bacteria
## 123 Fecal>CloacalSwab 0.96390562 DESeq2 man. geoMeans (ds2) Bacteria
## 124 Fecal>CloacalSwab 0.54121352 DESeq2 man. geoMeans (ds2) Bacteria
## 125 Fecal>CloacalSwab 0.73586574 DESeq2 man. geoMeans (ds2) Bacteria
## 126 Fecal>CloacalSwab 0.50208662 DESeq2 man. geoMeans (ds2) Bacteria
## 127 Fecal>CloacalSwab 0.55194855 DESeq2 man. geoMeans (ds2) Bacteria
## 128 CloacalSwab>Fecal 0.62105921 DESeq2 man. geoMeans (ds2) Bacteria
## 129 Fecal>CloacalSwab 0.27066597 DESeq2 man. geoMeans (ds2) Bacteria
## 130 Fecal>CloacalSwab 0.70594620 DESeq2 man. geoMeans (ds2) Bacteria
## 131 Fecal>CloacalSwab 0.55194855 DESeq2 man. geoMeans (ds2) Bacteria
## 132 Fecal>CloacalSwab 0.36859448 DESeq2 man. geoMeans (ds2) Bacteria
## 133 CloacalSwab>Fecal 0.93659949 DESeq2 man. geoMeans (ds2) <NA>
## Phylum Class
## 1 Proteobacteria Gammaproteobacteria
## 2 Proteobacteria Alphaproteobacteria
## 3 Actinobacteria Actinobacteria
## 4 Actinobacteria Actinobacteria
## 5 Firmicutes Bacilli
## 6 Proteobacteria Gammaproteobacteria
## 7 Actinobacteria Actinobacteria
## 8 Acidobacteria Acidobacteria_Gp16
## 9 Actinobacteria Actinobacteria
## 10 Proteobacteria Alphaproteobacteria
## 11 Actinobacteria Actinobacteria
## 12 Actinobacteria Actinobacteria
## 13 Cyanobacteria/Chloroplast Chloroplast
## 14 Actinobacteria Actinobacteria
## 15 Proteobacteria Gammaproteobacteria
## 16 Actinobacteria Actinobacteria
## 17 Actinobacteria Actinobacteria
## 18 Actinobacteria Actinobacteria
## 19 Chloroflexi Thermomicrobia
## 20 Actinobacteria Actinobacteria
## 21 Firmicutes Erysipelotrichia
## 22 Chloroflexi Chloroflexia
## 23 Proteobacteria Gammaproteobacteria
## 24 Actinobacteria Actinobacteria
## 25 Actinobacteria Actinobacteria
## 26 Actinobacteria Actinobacteria
## 27 Firmicutes Bacilli
## 28 Actinobacteria Actinobacteria
## 29 Actinobacteria Actinobacteria
## 30 Actinobacteria Actinobacteria
## 31 Actinobacteria Actinobacteria
## 32 Actinobacteria Actinobacteria
## 33 Actinobacteria Actinobacteria
## 34 Actinobacteria Actinobacteria
## 35 Actinobacteria Actinobacteria
## 36 Firmicutes Erysipelotrichia
## 37 Proteobacteria Alphaproteobacteria
## 38 Proteobacteria Gammaproteobacteria
## 39 Actinobacteria Actinobacteria
## 40 Firmicutes Clostridia
## 41 Actinobacteria Actinobacteria
## 42 Actinobacteria Actinobacteria
## 43 Actinobacteria Actinobacteria
## 44 Proteobacteria Alphaproteobacteria
## 45 Firmicutes Bacilli
## 46 Proteobacteria Alphaproteobacteria
## 47 Actinobacteria Actinobacteria
## 48 Actinobacteria Actinobacteria
## 49 Proteobacteria Alphaproteobacteria
## 50 Proteobacteria Gammaproteobacteria
## 51 Firmicutes Clostridia
## 52 Firmicutes Clostridia
## 53 Firmicutes Clostridia
## 54 Actinobacteria Actinobacteria
## 55 Chloroflexi Thermomicrobia
## 56 Actinobacteria Actinobacteria
## 57 Actinobacteria Actinobacteria
## 58 Firmicutes Clostridia
## 59 Firmicutes Clostridia
## 60 Firmicutes Clostridia
## 61 Actinobacteria Actinobacteria
## 62 Cyanobacteria/Chloroplast Chloroplast
## 63 Actinobacteria Actinobacteria
## 64 Firmicutes Bacilli
## 65 Actinobacteria Actinobacteria
## 66 Proteobacteria Alphaproteobacteria
## 67 Proteobacteria Alphaproteobacteria
## 68 Actinobacteria Actinobacteria
## 69 Actinobacteria Actinobacteria
## 70 Actinobacteria Actinobacteria
## 71 Proteobacteria Alphaproteobacteria
## 72 Firmicutes Erysipelotrichia
## 73 Actinobacteria Actinobacteria
## 74 Bacteroidetes Bacteroidia
## 75 Proteobacteria Gammaproteobacteria
## 76 Actinobacteria Actinobacteria
## 77 Actinobacteria Actinobacteria
## 78 Actinobacteria Actinobacteria
## 79 Actinobacteria Actinobacteria
## 80 Actinobacteria Actinobacteria
## 81 Proteobacteria Betaproteobacteria
## 82 Proteobacteria Gammaproteobacteria
## 83 Firmicutes Clostridia
## 84 Proteobacteria Gammaproteobacteria
## 85 Proteobacteria Alphaproteobacteria
## 86 Actinobacteria Actinobacteria
## 87 Actinobacteria Actinobacteria
## 88 Actinobacteria Actinobacteria
## 89 Proteobacteria Alphaproteobacteria
## 90 Proteobacteria Gammaproteobacteria
## 91 Actinobacteria Actinobacteria
## 92 Firmicutes Clostridia
## 93 Proteobacteria Alphaproteobacteria
## 94 Actinobacteria Actinobacteria
## 95 Proteobacteria Alphaproteobacteria
## 96 Proteobacteria Gammaproteobacteria
## 97 Actinobacteria Actinobacteria
## 98 Proteobacteria Alphaproteobacteria
## 99 Proteobacteria Alphaproteobacteria
## 100 Actinobacteria Actinobacteria
## 101 Actinobacteria Actinobacteria
## 102 Actinobacteria Actinobacteria
## 103 Firmicutes Bacilli
## 104 Cyanobacteria/Chloroplast Chloroplast
## 105 Proteobacteria Alphaproteobacteria
## 106 Chloroflexi <NA>
## 107 Actinobacteria Actinobacteria
## 108 Actinobacteria Actinobacteria
## 109 Actinobacteria Actinobacteria
## 110 Actinobacteria Actinobacteria
## 111 Actinobacteria Actinobacteria
## 112 Actinobacteria Actinobacteria
## 113 Actinobacteria Actinobacteria
## 114 Proteobacteria Gammaproteobacteria
## 115 Actinobacteria Actinobacteria
## 116 Actinobacteria Actinobacteria
## 117 Proteobacteria Gammaproteobacteria
## 118 Verrucomicrobia Spartobacteria
## 119 Actinobacteria Actinobacteria
## 120 Firmicutes Clostridia
## 121 Bacteroidetes Bacteroidia
## 122 Proteobacteria Alphaproteobacteria
## 123 Verrucomicrobia Spartobacteria
## 124 Proteobacteria Alphaproteobacteria
## 125 Proteobacteria Alphaproteobacteria
## 126 Proteobacteria Gammaproteobacteria
## 127 Actinobacteria Actinobacteria
## 128 Cyanobacteria/Chloroplast Chloroplast
## 129 Firmicutes Erysipelotrichia
## 130 Firmicutes Clostridia
## 131 Proteobacteria Alphaproteobacteria
## 132 Firmicutes Clostridia
## 133 <NA> <NA>
## Order Family
## 1 Oceanospirillales Halomonadaceae
## 2 Rhizobiales Methylobacteriaceae
## 3 Solirubrobacterales Solirubrobacteraceae
## 4 Actinomycetales Pseudonocardiaceae
## 5 Lactobacillales Carnobacteriaceae
## 6 Enterobacteriales Enterobacteriaceae
## 7 Solirubrobacterales <NA>
## 8 Gp16 <NA>
## 9 Solirubrobacterales Solirubrobacteraceae
## 10 Rhizobiales Xanthobacteraceae
## 11 Solirubrobacterales Solirubrobacteraceae
## 12 Actinomycetales Nocardioidaceae
## 13 Chloroplast Streptophyta
## 14 Actinomycetales Microbacteriaceae
## 15 Enterobacteriales Enterobacteriaceae
## 16 Rubrobacterales Rubrobacteraceae
## 17 Actinomycetales Mycobacteriaceae
## 18 Actinomycetales Microbacteriaceae
## 19 Sphaerobacterales Sphaerobacteraceae
## 20 Solirubrobacterales Solirubrobacteraceae
## 21 Erysipelotrichales Erysipelotrichaceae
## 22 Kallotenuales Kallotenuaceae
## 23 Enterobacteriales Enterobacteriaceae
## 24 Actinomycetales Mycobacteriaceae
## 25 Actinomycetales Nocardioidaceae
## 26 Actinomycetales Microbacteriaceae
## 27 Bacillales Bacillaceae_1
## 28 Actinomycetales Microbacteriaceae
## 29 Actinomycetales Microbacteriaceae
## 30 Actinomycetales Intrasporangiaceae
## 31 Actinomycetales Nocardioidaceae
## 32 Actinomycetales Microbacteriaceae
## 33 Rubrobacterales Rubrobacteraceae
## 34 Rubrobacterales Rubrobacteraceae
## 35 Actinomycetales Mycobacteriaceae
## 36 Erysipelotrichales Erysipelotrichaceae
## 37 Rhizobiales Methylobacteriaceae
## 38 Pseudomonadales Pseudomonadaceae
## 39 Actinomycetales Nocardioidaceae
## 40 Clostridiales Lachnospiraceae
## 41 Actinomycetales Nocardioidaceae
## 42 Solirubrobacterales Solirubrobacteraceae
## 43 Actinomycetales Propionibacteriaceae
## 44 Rhizobiales Methylobacteriaceae
## 45 Lactobacillales Enterococcaceae
## 46 Rhizobiales Bradyrhizobiaceae
## 47 Actinomycetales Nocardioidaceae
## 48 Actinomycetales Nocardioidaceae
## 49 Rhizobiales Methylobacteriaceae
## 50 Enterobacteriales Enterobacteriaceae
## 51 Clostridiales Ruminococcaceae
## 52 Clostridiales Eubacteriaceae
## 53 Clostridiales Lachnospiraceae
## 54 Gaiellales Gaiellaceae
## 55 Sphaerobacterales Sphaerobacteraceae
## 56 Actinomycetales Pseudonocardiaceae
## 57 Actinomycetales Pseudonocardiaceae
## 58 Clostridiales Ruminococcaceae
## 59 Clostridiales Lachnospiraceae
## 60 Clostridiales Lachnospiraceae
## 61 Actinomycetales Propionibacteriaceae
## 62 Chloroplast Streptophyta
## 63 Actinomycetales <NA>
## 64 Lactobacillales Carnobacteriaceae
## 65 Solirubrobacterales Patulibacteraceae
## 66 Alphaproteobacteria_incertae_sedis Geminicoccus
## 67 Rhizobiales Rhodobiaceae
## 68 Actinomycetales Streptomycetaceae
## 69 <NA> <NA>
## 70 Actinomycetales Propionibacteriaceae
## 71 Rhizobiales Methylobacteriaceae
## 72 Erysipelotrichales Erysipelotrichaceae
## 73 Solirubrobacterales <NA>
## 74 Bacteroidales Bacteroidaceae
## 75 Pseudomonadales Pseudomonadaceae
## 76 Actinomycetales Pseudonocardiaceae
## 77 Actinomycetales Mycobacteriaceae
## 78 Actinomycetales Cellulomonadaceae
## 79 Actinomycetales Pseudonocardiaceae
## 80 Solirubrobacterales Conexibacteraceae
## 81 Burkholderiales Alcaligenaceae
## 82 Pseudomonadales Pseudomonadaceae
## 83 Clostridiales Ruminococcaceae
## 84 Pseudomonadales Moraxellaceae
## 85 Rhizobiales Methylobacteriaceae
## 86 Actinomycetales <NA>
## 87 Actinomycetales Pseudonocardiaceae
## 88 Actinomycetales Microbacteriaceae
## 89 Rhizobiales Methylobacteriaceae
## 90 Enterobacteriales Enterobacteriaceae
## 91 Actinomycetales Kineosporiaceae
## 92 Clostridiales Ruminococcaceae
## 93 Alphaproteobacteria_incertae_sedis Geminicoccus
## 94 Actinomycetales Micrococcaceae
## 95 Rhodospirillales Reyranella
## 96 Pseudomonadales Moraxellaceae
## 97 Coriobacteriales Coriobacteriaceae
## 98 Rhizobiales Hyphomicrobiaceae
## 99 Rhizobiales Methylobacteriaceae
## 100 Solirubrobacterales Solirubrobacteraceae
## 101 Actinomycetales Sanguibacteraceae
## 102 Actinomycetales <NA>
## 103 Bacillales Staphylococcaceae
## 104 Chloroplast Chlorophyta
## 105 Sphingomonadales Sphingomonadaceae
## 106 <NA> <NA>
## 107 Actinomycetales Mycobacteriaceae
## 108 Actinomycetales Nocardiaceae
## 109 Actinomycetales Pseudonocardiaceae
## 110 Actinomycetales Pseudonocardiaceae
## 111 Acidimicrobiales Acidimicrobiaceae
## 112 Actinomycetales Propionibacteriaceae
## 113 Actinomycetales Propionibacteriaceae
## 114 Pseudomonadales Pseudomonadaceae
## 115 Acidimicrobiales Acidimicrobiaceae
## 116 Actinomycetales Intrasporangiaceae
## 117 Pseudomonadales Pseudomonadaceae
## 118 Terrimicrobium <NA>
## 119 Gaiellales Gaiellaceae
## 120 Clostridiales Ruminococcaceae
## 121 Bacteroidales Bacteroidaceae
## 122 Alphaproteobacteria_incertae_sedis Geminicoccus
## 123 Terrimicrobium <NA>
## 124 Rhizobiales Hyphomicrobiaceae
## 125 Alphaproteobacteria_incertae_sedis Geminicoccus
## 126 Enterobacteriales Enterobacteriaceae
## 127 Solirubrobacterales <NA>
## 128 Chloroplast Streptophyta
## 129 Erysipelotrichales Erysipelotrichaceae
## 130 Clostridiales Lachnospiraceae
## 131 Alphaproteobacteria_incertae_sedis Geminicoccus
## 132 Clostridiales Lachnospiraceae
## 133 <NA> <NA>
## Genus Species
## 1 Salinicola socius
## 2 Methylobacterium <NA>
## 3 Solirubrobacter <NA>
## 4 Actinomycetospora <NA>
## 5 Carnobacterium <NA>
## 6 Buttiauxella <NA>
## 7 <NA> <NA>
## 8 <NA> <NA>
## 9 Solirubrobacter <NA>
## 10 <NA> <NA>
## 11 Solirubrobacter <NA>
## 12 Nocardioides <NA>
## 13 <NA> <NA>
## 14 Agrococcus <NA>
## 15 <NA> <NA>
## 16 Rubrobacter <NA>
## 17 Mycobacterium <NA>
## 18 Curtobacterium <NA>
## 19 <NA> <NA>
## 20 Solirubrobacter <NA>
## 21 Clostridium_XVIII <NA>
## 22 Kallotenue <NA>
## 23 Enterobacter <NA>
## 24 Mycobacterium JC2972
## 25 Nocardioides <NA>
## 26 Microbacterium <NA>
## 27 Bacillus <NA>
## 28 Curtobacterium <NA>
## 29 Microbacterium <NA>
## 30 <NA> <NA>
## 31 Nocardioides <NA>
## 32 Pseudoclavibacter helvolus
## 33 Rubrobacter <NA>
## 34 Rubrobacter <NA>
## 35 Mycobacterium <NA>
## 36 Clostridium_XVIII <NA>
## 37 <NA> <NA>
## 38 Pseudomonas <NA>
## 39 Nocardioides <NA>
## 40 <NA> <NA>
## 41 Nocardioides ginsengagri
## 42 Solirubrobacter <NA>
## 43 Microlunatus <NA>
## 44 <NA> <NA>
## 45 Enterococcus <NA>
## 46 Bradyrhizobium <NA>
## 47 Nocardioides <NA>
## 48 Nocardioides <NA>
## 49 Methylobacterium <NA>
## 50 Pantoea <NA>
## 51 Intestinimonas <NA>
## 52 Anaerofustis <NA>
## 53 <NA> <NA>
## 54 Gaiella <NA>
## 55 Sphaerobacter <NA>
## 56 Actinomycetospora <NA>
## 57 Pseudonocardia <NA>
## 58 <NA> <NA>
## 59 Clostridium_XlVa <NA>
## 60 <NA> <NA>
## 61 Microlunatus <NA>
## 62 <NA> <NA>
## 63 <NA> <NA>
## 64 Catellicoccus <NA>
## 65 Patulibacter minatonensis
## 66 <NA> <NA>
## 67 <NA> <NA>
## 68 Streptomyces <NA>
## 69 <NA> <NA>
## 70 Friedmanniella <NA>
## 71 Microvirga zambiensis
## 72 Clostridium_XVIII <NA>
## 73 <NA> <NA>
## 74 Bacteroides <NA>
## 75 Pseudomonas <NA>
## 76 Pseudonocardia <NA>
## 77 Mycobacterium <NA>
## 78 Cellulomonas <NA>
## 79 Pseudonocardia <NA>
## 80 Conexibacter <NA>
## 81 Achromobacter <NA>
## 82 Pseudomonas <NA>
## 83 Anaerofilum <NA>
## 84 Acinetobacter <NA>
## 85 Microvirga <NA>
## 86 <NA> <NA>
## 87 Pseudonocardia endophytica
## 88 Microbacterium <NA>
## 89 Microvirga <NA>
## 90 Pantoea <NA>
## 91 Pseudokineococcus <NA>
## 92 Clostridium_IV <NA>
## 93 <NA> <NA>
## 94 Arthrobacter <NA>
## 95 <NA> <NA>
## 96 Acinetobacter <NA>
## 97 Denitrobacterium <NA>
## 98 Rhodoplanes <NA>
## 99 Microvirga lotononidis
## 100 Solirubrobacter <NA>
## 101 Sanguibacter <NA>
## 102 <NA> <NA>
## 103 Staphylococcus <NA>
## 104 <NA> <NA>
## 105 Sphingopyxis <NA>
## 106 <NA> <NA>
## 107 Mycobacterium <NA>
## 108 Rhodococcus <NA>
## 109 Actinomycetospora chiangmaiensis
## 110 Actinomycetospora <NA>
## 111 Ilumatobacter <NA>
## 112 <NA> <NA>
## 113 Friedmanniella <NA>
## 114 Pseudomonas <NA>
## 115 Ilumatobacter <NA>
## 116 Janibacter <NA>
## 117 Pseudomonas <NA>
## 118 <NA> <NA>
## 119 Gaiella <NA>
## 120 Intestinimonas <NA>
## 121 Bacteroides <NA>
## 122 <NA> <NA>
## 123 <NA> <NA>
## 124 Pedomicrobium <NA>
## 125 <NA> <NA>
## 126 Pantoea <NA>
## 127 <NA> <NA>
## 128 <NA> <NA>
## 129 Clostridium_XVIII <NA>
## 130 Clostridium_XlVa <NA>
## 131 <NA> <NA>
## 132 <NA> <NA>
## 133 <NA> <NA>res.ds2[res.ds2$pval.adj < 0.05,"Feature"]## [1] NA NA "ASV12" NA "ASV20" NA NA NA "ASV8"## Three ASVs sig: ASV20, ASV12 and ASV8
## NOTE: some tests don't give back pval.adj values and I just don't use them...
### We can conclude that ASV20 and ASV8 are probably really differentially abundant between fecals and cloacal samples. Let's plot
featurePlot(TxODA, predictor = "Type", feature = "ASV20") + theme_classic()
featurePlot(TxODA, predictor = "Type", feature = "ASV8") + theme_classic()
## Could do this at genus level and phylum level. In between I generally can't think of how to interpret it biologically. You need to merge_taxa and I would suggest when you do that
## Let's do genus level
## I just use the same tests that worked on the ASV level data for the higher taxa levels. There is not enough information when you merge at higher taxonmic scales to evalutae the methods
TxO_G <- tax_glom(TxO, taxrank = "Genus")
TxO_G## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 155 taxa and 12 samples ]
## sample_data() Sample Data: [ 12 samples by 21 sample variables ]
## tax_table() Taxonomy Table: [ 155 taxa by 7 taxonomic ranks ]
## refseq() DNAStringSet: [ 155 reference sequences ]## Testing 199 genera
res.liaG <- DA.lia(TxO_G, predictor = "Type")
res.liaG## Feature logFC AveExpr t pval
## 1 ASV10 -1.497860867 0.5032085171 -1.70254422 1.133706e-01
## 2 ASV100 -1.791852439 1.1394818842 -5.26658693 1.736398e-04
## 3 ASV1003 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 4 ASV101 -0.653397888 0.0809770274 -1.57914060 1.392641e-01
## 5 ASV1027 0.379378705 0.1896893523 1.03411076 3.206770e-01
## 6 ASV103 1.953201765 1.9924328463 3.62812975 3.251163e-03
## 7 ASV1046 0.094441978 0.0280811122 0.31347005 7.590927e-01
## 8 ASV1067 -0.479336507 -0.0060536631 -1.67486793 1.187778e-01
## 9 ASV107 -0.756823436 0.1326898015 -2.40793760 3.229339e-02
## 10 ASV11 -0.218481214 1.1073250019 -0.20288488 8.424900e-01
## 11 ASV1108 -0.342106570 -0.0746686315 -1.60067567 1.344054e-01
## 12 ASV1122 -0.454773782 -0.0183350252 -1.68079470 1.176012e-01
## 13 ASV113 -0.899621845 0.2040890059 -2.43208398 3.088179e-02
## 14 ASV1136 0.168109523 0.2480169328 0.53050648 6.050577e-01
## 15 ASV1179 0.195118023 0.1939436652 0.63638391 5.360056e-01
## 16 ASV1184 -0.189099660 -0.1511720862 -0.95328051 3.585189e-01
## 17 ASV121 -0.272046078 0.6053767006 -0.47579977 6.424245e-01
## 18 ASV1211 -0.336683339 -0.0773802469 -1.58950612 1.369068e-01
## 19 ASV1214 -0.342106570 -0.0746686315 -1.60067567 1.344054e-01
## 20 ASV1241 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 21 ASV1262 0.152715122 0.0763575610 0.81044921 4.328412e-01
## 22 ASV129 -1.167366387 0.3379612769 -2.01629914 6.577952e-02
## 23 ASV1318 0.390820762 0.1954103808 0.97607186 3.475407e-01
## 24 ASV134 0.600708100 0.3003540502 0.93083714 3.695668e-01
## 25 ASV1382 0.115524530 0.0577622650 0.51013770 6.188427e-01
## 26 ASV139 -1.030914209 0.3553246675 -2.11830835 5.480872e-02
## 27 ASV1397 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 28 ASV1400 -0.406826770 -0.0423085312 -1.67473056 1.188052e-01
## 29 ASV1427 -0.187487526 -0.0937437629 -0.77706654 4.515749e-01
## 30 ASV1443 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 31 ASV1470 -0.098851296 -0.0685655248 -0.37177625 7.162852e-01
## 32 ASV1473 -0.182988729 -0.0914943647 -1.17462603 2.620360e-01
## 33 ASV1485 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 34 ASV15 -0.615543612 0.8318431218 -0.81481409 4.304295e-01
## 35 ASV155 -0.907244155 0.2079001611 -1.68854314 1.160783e-01
## 36 ASV1553 -0.183897906 -0.0919489529 -0.81892314 4.281672e-01
## 37 ASV170 -0.352753146 0.6255976632 -0.67105611 5.143939e-01
## 38 ASV175 0.389672973 0.1756966099 0.62333279 5.442720e-01
## 39 ASV176 -1.012706731 0.2606314490 -2.11738320 5.490005e-02
## 40 ASV177 -0.131120698 1.0349337602 -0.20226952 8.429607e-01
## 41 ASV1787 -0.223724722 -0.1338595553 -1.13822463 2.763722e-01
## 42 ASV1814 -0.303012056 -0.1515060279 -1.46166489 1.685129e-01
## 43 ASV1826 -0.273077005 -0.1365385026 -1.28909248 2.207105e-01
## 44 ASV1828 -0.454773782 -0.0183350252 -1.68079470 1.176012e-01
## 45 ASV183 -0.149480027 1.7789888662 -0.21056077 8.366234e-01
## 46 ASV185 -0.456955388 -0.0172442223 -1.68046010 1.176673e-01
## 47 ASV19 0.327216177 1.9763112709 0.30149227 7.679960e-01
## 48 ASV190 -0.749069574 0.1288128707 -2.49686921 2.738194e-02
## 49 ASV1922 -0.119910008 -0.0599550039 -0.78757017 4.456255e-01
## 50 ASV193 2.042283513 1.2323752282 6.82382038 1.491243e-05
## 51 ASV2 -1.168831040 0.5168380018 -1.17431059 2.621577e-01
## 52 ASV204 -0.230298784 0.3163767201 -0.47589410 6.423591e-01
## 53 ASV207 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 54 ASV214 -1.548021105 0.5282886363 -3.69530668 2.867265e-03
## 55 ASV215 1.889548930 1.0411591187 7.54946793 5.308128e-06
## 56 ASV216 -0.495077422 0.0018167945 -1.55788238 1.442084e-01
## 57 ASV2179 -0.158461652 -0.0792308258 -0.70031038 4.965616e-01
## 58 ASV221 -0.137530445 0.4738357071 -0.25299600 8.043841e-01
## 59 ASV223 0.640878384 0.2958760844 2.30443517 3.907631e-02
## 60 ASV226 -0.760204026 0.1343800968 -1.52042028 1.532894e-01
## 61 ASV23 0.198899672 1.2821213233 0.18059853 8.595777e-01
## 62 ASV235 -0.746067399 0.1273117830 -1.92698478 7.701798e-02
## 63 ASV236 -0.628942549 0.0687493579 -1.90699541 7.976302e-02
## 64 ASV237 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 65 ASV248 0.444217799 0.2221088995 1.42803210 1.777877e-01
## 66 ASV253 0.914877279 0.7171097528 2.40819275 3.227815e-02
## 67 ASV256 0.019695794 0.2217570805 0.04548985 9.644355e-01
## 68 ASV258 -0.341430858 -0.0750064873 -1.59933294 1.347040e-01
## 69 ASV26 -0.882767916 2.3642085665 -1.84760021 8.845337e-02
## 70 ASV265 -0.721940194 0.1152481805 -1.86790390 8.539047e-02
## 71 ASV267 0.960848500 0.7856027313 1.71471355 1.110627e-01
## 72 ASV270 -0.938362342 0.2234592545 -1.67899947 1.179565e-01
## 73 ASV273 -0.862449754 0.1855029604 -1.73610889 1.071059e-01
## 74 ASV274 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 75 ASV29 -0.013447636 0.7234288721 -0.01600085 9.874863e-01
## 76 ASV291 -0.786352817 0.1474544920 -1.99943867 6.777787e-02
## 77 ASV293 -0.226582040 -0.1324308965 -1.15416697 2.700211e-01
## 78 ASV296 -0.550224686 0.0293904267 -1.64101440 1.256995e-01
## 79 ASV30 -0.776840056 1.3341914068 -1.05444168 3.116358e-01
## 80 ASV303 0.380282447 0.1462933816 1.12864447 2.802435e-01
## 81 ASV306 -0.037389965 0.1452671892 -0.12562792 9.020244e-01
## 82 ASV31 -1.815486676 0.7897521651 -2.17367324 4.959497e-02
## 83 ASV311 -0.470113727 -0.0106650531 -1.56405566 1.427573e-01
## 84 ASV312 -0.553340042 0.0309481045 -2.02241562 6.506814e-02
## 85 ASV318 0.243103789 0.1215518947 0.69695586 4.985875e-01
## 86 ASV332 0.179684732 0.7243443797 0.30563936 7.649094e-01
## 87 ASV334 -0.494145980 0.0013510737 -1.66939636 1.198733e-01
## 88 ASV337 0.407027207 0.2035136035 0.79505489 4.414168e-01
## 89 ASV338 -0.525516721 0.0170364441 -1.54751982 1.466729e-01
## 90 ASV342 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 91 ASV348 -0.574218104 0.1014270424 -1.31661676 2.115964e-01
## 92 ASV349 0.399747088 0.1998735440 1.11890340 2.842221e-01
## 93 ASV356 -0.316867751 -0.0872880411 -1.46456435 1.677328e-01
## 94 ASV359 -0.497788193 0.0031721801 -1.55770542 1.442502e-01
## 95 ASV375 0.120115498 0.9528485497 0.25522307 8.027018e-01
## 96 ASV376 -0.179621493 0.0495883175 -0.66003276 5.212094e-01
## 97 ASV378 -0.226582040 -0.1324308965 -1.15416697 2.700211e-01
## 98 ASV381 -0.301699058 0.1088215841 -1.00159122 3.355360e-01
## 99 ASV387 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 100 ASV388 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 101 ASV416 0.237395425 0.1186977125 0.61880002 5.471596e-01
## 102 ASV43 -0.332100429 1.8570550387 -0.54383977 5.961185e-01
## 103 ASV44 0.108549090 1.4863015379 0.17116720 8.668319e-01
## 104 ASV440 -0.552111831 -0.0269561402 -1.81420219 9.370823e-02
## 105 ASV445 -0.491964374 0.0002602708 -1.67027530 1.196968e-01
## 106 ASV451 1.676186719 1.0500025433 4.77666681 4.029273e-04
## 107 ASV467 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 108 ASV472 0.321693253 0.1608466267 1.01696835 3.284486e-01
## 109 ASV473 -0.226582040 -0.1324308965 -1.15416697 2.700211e-01
## 110 ASV474 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 111 ASV475 -0.221013951 -0.1352149408 -1.12296227 2.825591e-01
## 112 ASV483 -0.241725340 0.3084818359 -0.47205386 6.450223e-01
## 113 ASV506 -0.409008376 -0.0412177283 -1.67569224 1.186136e-01
## 114 ASV517 -0.580215026 0.0443855965 -1.98702309 6.928506e-02
## 115 ASV520 -0.475006104 -0.0082188646 -1.58837188 1.371630e-01
## 116 ASV53 -2.158895989 0.8337260779 -2.89040906 1.308004e-02
## 117 ASV531 -0.301884141 0.0609671132 -1.24021642 2.376711e-01
## 118 ASV538 -0.548043080 0.0282996238 -1.64226147 1.254384e-01
## 119 ASV54 0.869559098 0.8152888846 1.03254157 3.213827e-01
## 120 ASV558 -0.456955388 -0.0172442223 -1.68046010 1.176673e-01
## 121 ASV562 0.439080054 0.2195400271 1.21301026 2.475511e-01
## 122 ASV566 -0.683071847 0.0958140071 -2.09412069 5.724388e-02
## 123 ASV572 -0.485160325 -0.0031417538 -2.01139332 6.635528e-02
## 124 ASV585 0.509339241 0.2546696205 1.10863003 2.884644e-01
## 125 ASV586 -0.030386926 -0.0151934631 -0.11381975 9.111884e-01
## 126 ASV588 -0.437039177 -0.0545574169 -1.69628154 1.145748e-01
## 127 ASV593 -0.006813863 0.1837942870 -0.02572862 9.798800e-01
## 128 ASV6 -2.664992519 1.5178175340 -3.42567285 4.756006e-03
## 129 ASV60 -0.924410093 0.2164831302 -1.44947211 1.718271e-01
## 130 ASV605 0.783533594 0.7192005109 1.06063723 3.089185e-01
## 131 ASV62 -1.965051964 0.7760935871 -3.40169302 4.975893e-03
## 132 ASV641 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 133 ASV645 -0.460885078 0.1903334284 -1.15293590 2.705075e-01
## 134 ASV647 -0.245721916 -0.1228609582 -1.25644812 2.319270e-01
## 135 ASV652 1.027586133 0.5137930663 2.83318738 1.457213e-02
## 136 ASV665 -0.285484314 -0.1029797595 -1.30449250 2.155728e-01
## 137 ASV68 0.849823366 1.6663464942 1.59854441 1.348796e-01
## 138 ASV69 0.559688018 0.8747211983 1.01839068 3.277986e-01
## 139 ASV716 -0.138412599 -0.0883461759 -0.86973339 4.008388e-01
## 140 ASV728 1.086213726 0.7550160468 2.90609890 1.269804e-02
## 141 ASV776 -0.522351300 0.0154537338 -1.65625765 1.225403e-01
## 142 ASV784 0.034452168 0.0172260842 0.21331744 8.345190e-01
## 143 ASV785 -0.336683339 -0.0773802469 -1.58950612 1.369068e-01
## 144 ASV8 -3.777785435 2.2885419296 -7.24424184 8.130275e-06
## 145 ASV809 -0.065358292 -0.0326791459 -0.23334325 8.192724e-01
## 146 ASV81 0.400730829 0.6963992804 0.55866110 5.862617e-01
## 147 ASV82 -1.617191100 0.5628736335 -2.76111643 1.669245e-02
## 148 ASV850 0.238304601 0.1191523007 1.23580713 2.392508e-01
## 149 ASV87 0.300784793 1.1608074047 0.36979140 7.177271e-01
## 150 ASV9 -0.103933099 0.7594946773 -0.08889676 9.305724e-01
## 151 ASV906 0.111139052 0.0555695262 0.35807593 7.262605e-01
## 152 ASV91 1.133460036 1.8158082017 1.50357766 1.575288e-01
## 153 ASV95 -0.788025039 0.1482906032 -1.50677547 1.567163e-01
## 154 ASV999 -0.180882822 -0.0904414110 -0.83694900 4.183349e-01
## pval.adj B ordering Method Kingdom
## 1 0.3641727070 -5.2116738 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 2 0.0066851314 1.0293298 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 3 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 4 0.3641727070 -5.3875434 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 5 0.4713613195 -6.0471020 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 6 0.0715255890 -1.8651274 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 7 0.8508732289 -6.5386524 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 8 0.3641727070 -5.2518441 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 9 0.2925401085 -4.0759136 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 10 0.8891503738 -6.5687462 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 11 0.3641727070 -5.3574666 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 12 0.3641727070 -5.2432762 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 13 0.2925401085 -4.0340953 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 14 0.7223169051 -6.4430448 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 15 0.6656843327 -6.3793442 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 16 0.5019265231 -6.1258875 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 17 0.7468679745 -6.4716316 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 18 0.3641727070 -5.3730998 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 19 0.3641727070 -5.3574666 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 20 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 21 0.5746339932 -6.2513974 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 22 0.3641727070 -4.7302417 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 23 0.4910208050 -6.1042262 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 24 0.5127323435 -6.1467865 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 25 0.7330906101 -6.4540367 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 26 0.3641727070 -4.5647163 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 27 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 28 0.3641727070 -5.2520424 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 29 0.5843910393 -6.2781127 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 30 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 31 0.8187405040 -6.5176781 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 32 0.4339390419 -5.8975462 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 33 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 34 0.5746339932 -6.2478296 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 35 0.3641727070 -5.2320465 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 36 0.5746339932 -6.2444551 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 37 0.6493169424 -6.3561270 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 38 0.6687505828 -6.3877851 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 39 0.3641727070 -4.5662347 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 40 0.8891503738 -6.5688779 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 41 0.4377019993 -5.9377760 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 42 0.3873281729 -5.5467539 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 43 0.4105374648 -5.7645809 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 44 0.3641727070 -5.2432762 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 45 0.8891503738 -6.5670700 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 46 0.3641727070 -5.2437604 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 47 0.8508732289 -6.5425266 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 48 0.2925401085 -3.9212094 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 49 0.5815790013 -6.2698163 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 50 0.0007655049 3.4421663 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 51 0.4339390419 -5.8978992 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 52 0.7468679745 -6.4715849 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 53 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 54 0.0715255890 -1.7417685 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 55 0.0006260312 4.4431403 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 56 0.3641727070 -5.4169692 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 57 0.6345659031 -6.3356489 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 58 0.8785471616 -6.5566897 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 59 0.3343195832 -4.2534500 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 60 0.3732220856 -5.4681723 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 61 0.9005099833 -6.5732624 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 62 0.3641727070 -4.8718035 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 63 0.3641727070 -4.9030251 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 64 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 65 0.3968015085 -5.5907574 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 66 0.2925401085 -4.0754725 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 67 0.9771254762 -6.5894297 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 68 0.3641727070 -5.3593497 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 69 0.3641727070 -4.9947452 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 70 0.3641727070 -4.9635715 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 71 0.3641727070 -5.1938825 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 72 0.3641727070 -5.2458734 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 73 0.3641727070 -5.1624162 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 74 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 75 0.9874862784 -6.5903907 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 76 0.3641727070 -4.7572154 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 77 0.4339390419 -5.9202821 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 78 0.3641727070 -5.3004179 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 79 0.4659409472 -6.0264356 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 80 0.4377019993 -5.9481939 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 81 0.9322936973 -6.5821668 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 82 0.3641727070 -4.4733040 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 83 0.3641727070 -5.4084514 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 84 0.3641727070 -4.7204285 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 85 0.6345659031 -6.3380381 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 86 0.8508732289 -6.5412021 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 87 0.3641727070 -5.2597371 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 88 0.5810100965 -6.2638430 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 89 0.3643166587 -5.4312166 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 90 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 91 0.4105374648 -5.7311967 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 92 0.4377019993 -5.9587134 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 93 0.3873281729 -5.5429266 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 94 0.3641727070 -5.4172130 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 95 0.8785471616 -6.5560930 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 96 0.6525711010 -6.3636329 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 97 0.4339390419 -5.9202821 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 98 0.4784494553 -6.0794536 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 99 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 100 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 101 0.6687505828 -6.3906783 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 102 0.7172050847 -6.4356270 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 103 0.9019737361 -6.5750163 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 104 0.3641727070 -5.0456050 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 105 0.3641727070 -5.2584703 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 106 0.0124101602 0.1962344 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 107 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 108 0.4727204739 -6.0642646 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 109 0.4339390419 -5.9202821 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 110 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 111 0.4377019993 -5.9543392 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 112 0.7468679745 -6.4734796 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 113 0.3641727070 -5.2506536 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 114 0.3641727070 -4.7770052 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 115 0.3641727070 -5.3746833 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 116 0.1831206294 -3.2184805 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 117 0.4139845220 -5.8225315 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 118 0.3641727070 -5.2986397 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 119 0.4713613195 -6.0486830 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 120 0.3641727070 -5.2437604 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 121 0.4235874942 -5.8540355 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 122 0.3641727070 -4.6043142 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 123 0.3641727070 -4.7381018 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 124 0.4398368769 -5.9697273 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 125 0.9354867514 -6.5836637 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 126 0.3641727070 -5.2207993 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 127 0.9862844312 -6.5901755 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 128 0.0851430528 -2.2376078 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 129 0.3891379208 -5.5627900 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 130 0.4659409472 -6.0200711 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 131 0.0851430528 -2.2817555 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 132 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 133 0.4339390419 -5.9216400 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 134 0.4105374648 -5.8034773 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 135 0.1870089854 -3.3220995 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 136 0.4105374648 -5.7459679 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 137 0.3641727070 -5.3604551 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 138 0.4727204739 -6.0628498 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 139 0.5511533387 -6.2014799 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 140 0.1831206294 -3.1900043 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 141 0.3641727070 -5.2786241 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 142 0.8891503738 -6.5664529 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 143 0.3641727070 -5.3730998 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 144 0.0006260312 4.0313488 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 145 0.8885066780 -6.5617313 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 146 0.7109000350 -6.4271756 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 147 0.1977413366 -3.4520236 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 148 0.4139845220 -5.8276743 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 149 0.8187405040 -6.5184496 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 150 0.9490605851 -6.5863392 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 151 0.8223831904 -6.5229210 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 152 0.3732220856 -5.4909165 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 153 0.3732220856 -5.4866116 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 154 0.5701201829 -6.2294726 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## Phylum Class Order
## 1 Proteobacteria Gammaproteobacteria Oceanospirillales
## 2 Proteobacteria Alphaproteobacteria Rhizobiales
## 3 Actinobacteria Actinobacteria Actinomycetales
## 4 Bacteroidetes Sphingobacteriia Sphingobacteriales
## 5 Proteobacteria Alphaproteobacteria Rhizobiales
## 6 Actinobacteria Actinobacteria Solirubrobacterales
## 7 Actinobacteria Actinobacteria Actinomycetales
## 8 Proteobacteria Betaproteobacteria Burkholderiales
## 9 Firmicutes Bacilli Lactobacillales
## 10 Proteobacteria Gammaproteobacteria Enterobacteriales
## 11 Bacteroidetes Bacteroidia Bacteroidales
## 12 Fusobacteria Fusobacteriia Fusobacteriales
## 13 Bacteroidetes Flavobacteriia Flavobacteriales
## 14 Actinobacteria Actinobacteria Acidimicrobiales
## 15 Actinobacteria Actinobacteria Actinomycetales
## 16 Bacteroidetes Cytophagia Cytophagales
## 17 Actinobacteria Actinobacteria Actinomycetales
## 18 Bacteroidetes Flavobacteriia Flavobacteriales
## 19 Proteobacteria Alphaproteobacteria Rhizobiales
## 20 Actinobacteria Actinobacteria Bifidobacteriales
## 21 Proteobacteria Alphaproteobacteria Rhizobiales
## 22 Proteobacteria Betaproteobacteria Burkholderiales
## 23 Actinobacteria Actinobacteria Actinomycetales
## 24 Firmicutes Negativicutes Selenomonadales
## 25 Actinobacteria Actinobacteria Actinomycetales
## 26 Proteobacteria Alphaproteobacteria Rhizobiales
## 27 Proteobacteria Alphaproteobacteria Caulobacterales
## 28 Bacteroidetes Bacteroidia Bacteroidales
## 29 Nitrospirae Nitrospira Nitrospirales
## 30 Actinobacteria Actinobacteria Actinomycetales
## 31 Chloroflexi Chloroflexia Kallotenuales
## 32 Proteobacteria Deltaproteobacteria Myxococcales
## 33 Actinobacteria Actinobacteria Actinomycetales
## 34 Proteobacteria Gammaproteobacteria Enterobacteriales
## 35 Proteobacteria Betaproteobacteria Burkholderiales
## 36 Proteobacteria Alphaproteobacteria Rhodobacterales
## 37 Actinobacteria Actinobacteria Actinomycetales
## 38 Proteobacteria Deltaproteobacteria Desulfovibrionales
## 39 Actinobacteria Actinobacteria Actinomycetales
## 40 Firmicutes Bacilli Bacillales
## 41 Proteobacteria Betaproteobacteria Burkholderiales
## 42 Planctomycetes Planctomycetia Planctomycetales
## 43 Actinobacteria Actinobacteria Acidimicrobiales
## 44 Gemmatimonadetes Gemmatimonadetes Gemmatimonadales
## 45 Actinobacteria Actinobacteria Actinomycetales
## 46 Firmicutes Bacilli Bacillales
## 47 Firmicutes Bacilli Lactobacillales
## 48 Actinobacteria Actinobacteria Actinomycetales
## 49 Proteobacteria Deltaproteobacteria Myxococcales
## 50 Actinobacteria Actinobacteria Rubrobacterales
## 51 Proteobacteria Gammaproteobacteria Legionellales
## 52 Actinobacteria Actinobacteria Actinomycetales
## 53 Proteobacteria Gammaproteobacteria Enterobacteriales
## 54 Proteobacteria Alphaproteobacteria Sphingomonadales
## 55 Actinobacteria Actinobacteria Actinomycetales
## 56 Bacteroidetes Cytophagia Cytophagales
## 57 Proteobacteria Alphaproteobacteria Rhizobiales
## 58 Actinobacteria Actinobacteria Actinomycetales
## 59 Proteobacteria Alphaproteobacteria Rhizobiales
## 60 Firmicutes Bacilli Lactobacillales
## 61 Firmicutes Bacilli Lactobacillales
## 62 Bacteroidetes Sphingobacteriia Sphingobacteriales
## 63 Proteobacteria Gammaproteobacteria Alteromonadales
## 64 Actinobacteria Actinobacteria Actinomycetales
## 65 Firmicutes Clostridia Clostridiales
## 66 Actinobacteria Actinobacteria Gaiellales
## 67 Chloroflexi Thermomicrobia Sphaerobacterales
## 68 Deinococcus-Thermus Deinococci Thermales
## 69 Actinobacteria Actinobacteria Actinomycetales
## 70 Proteobacteria Alphaproteobacteria Rhizobiales
## 71 Firmicutes Clostridia Clostridiales
## 72 Actinobacteria Actinobacteria Actinomycetales
## 73 Proteobacteria Gammaproteobacteria Xanthomonadales
## 74 Bacteroidetes Sphingobacteriia Sphingobacteriales
## 75 Firmicutes Bacilli Lactobacillales
## 76 Proteobacteria Alphaproteobacteria Rhizobiales
## 77 Bacteroidetes Bacteroidia Bacteroidales
## 78 Firmicutes Clostridia Clostridiales
## 79 Actinobacteria Actinobacteria Solirubrobacterales
## 80 Actinobacteria Actinobacteria Actinomycetales
## 81 Actinobacteria Actinobacteria Actinomycetales
## 82 Actinobacteria Actinobacteria Actinomycetales
## 83 Firmicutes Clostridia Clostridiales
## 84 Firmicutes Bacilli Lactobacillales
## 85 Firmicutes Clostridia Clostridiales
## 86 Bacteroidetes Bacteroidia Bacteroidales
## 87 Firmicutes Bacilli Bacillales
## 88 Actinobacteria Actinobacteria Actinomycetales
## 89 Proteobacteria Alphaproteobacteria Rhodobacterales
## 90 Proteobacteria Gammaproteobacteria Xanthomonadales
## 91 Actinobacteria Actinobacteria Actinomycetales
## 92 Actinobacteria Actinobacteria Actinomycetales
## 93 Actinobacteria Actinobacteria Actinomycetales
## 94 Proteobacteria Betaproteobacteria Burkholderiales
## 95 Actinobacteria Actinobacteria Solirubrobacterales
## 96 Proteobacteria Betaproteobacteria Burkholderiales
## 97 Proteobacteria Alphaproteobacteria Rhizobiales
## 98 Firmicutes Clostridia Clostridiales
## 99 Proteobacteria Betaproteobacteria Burkholderiales
## 100 Firmicutes Erysipelotrichia Erysipelotrichales
## 101 Firmicutes Bacilli Bacillales
## 102 Actinobacteria Actinobacteria Actinomycetales
## 103 Actinobacteria Actinobacteria Actinomycetales
## 104 Proteobacteria Alphaproteobacteria Rhodobacterales
## 105 Bacteroidetes Flavobacteriia Flavobacteriales
## 106 Proteobacteria Alphaproteobacteria Rhizobiales
## 107 Proteobacteria Alphaproteobacteria Sphingomonadales
## 108 Actinobacteria Actinobacteria Actinomycetales
## 109 Firmicutes Clostridia Clostridiales
## 110 Actinobacteria Actinobacteria Actinomycetales
## 111 Proteobacteria Alphaproteobacteria Rhizobiales
## 112 Actinobacteria Actinobacteria Actinomycetales
## 113 Proteobacteria Alphaproteobacteria Sphingomonadales
## 114 Actinobacteria Actinobacteria Actinomycetales
## 115 Proteobacteria Alphaproteobacteria Rhodobacterales
## 116 Proteobacteria Gammaproteobacteria Pseudomonadales
## 117 Actinobacteria Actinobacteria Actinomycetales
## 118 Deinococcus-Thermus Deinococci Deinococcales
## 119 Actinobacteria Actinobacteria Coriobacteriales
## 120 Firmicutes Negativicutes Selenomonadales
## 121 Proteobacteria Alphaproteobacteria Rhizobiales
## 122 Actinobacteria Actinobacteria Actinomycetales
## 123 Proteobacteria Gammaproteobacteria Pseudomonadales
## 124 Actinobacteria Actinobacteria Acidimicrobiales
## 125 Firmicutes Bacilli Bacillales
## 126 Firmicutes Clostridia Clostridiales
## 127 Actinobacteria Actinobacteria Actinomycetales
## 128 Actinobacteria Actinobacteria Actinomycetales
## 129 Firmicutes Clostridia Clostridiales
## 130 Firmicutes Clostridia Clostridiales
## 131 Firmicutes Bacilli Bacillales
## 132 Proteobacteria Alphaproteobacteria Rhizobiales
## 133 Proteobacteria Alphaproteobacteria Rhizobiales
## 134 Proteobacteria Alphaproteobacteria Rhizobiales
## 135 Chloroflexi Thermomicrobia Sphaerobacterales
## 136 Proteobacteria Alphaproteobacteria Sphingomonadales
## 137 Actinobacteria Actinobacteria Actinomycetales
## 138 Actinobacteria Actinobacteria Actinomycetales
## 139 Firmicutes Clostridia Clostridiales
## 140 Actinobacteria Actinobacteria Acidimicrobiales
## 141 Proteobacteria Alphaproteobacteria Caulobacterales
## 142 Actinobacteria Actinobacteria Actinomycetales
## 143 Proteobacteria Betaproteobacteria Burkholderiales
## 144 Proteobacteria Gammaproteobacteria Pseudomonadales
## 145 Firmicutes Clostridia Clostridiales
## 146 Firmicutes Clostridia Clostridiales
## 147 Proteobacteria Gammaproteobacteria Xanthomonadales
## 148 Proteobacteria Alphaproteobacteria Rhizobiales
## 149 Proteobacteria Gammaproteobacteria Enterobacteriales
## 150 Firmicutes Bacilli Lactobacillales
## 151 Firmicutes Bacilli Bacillales
## 152 Firmicutes Erysipelotrichia Erysipelotrichales
## 153 Proteobacteria Gammaproteobacteria Oceanospirillales
## 154 Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species
## 1 Halomonadaceae Salinicola <NA>
## 2 Methylobacteriaceae Methylobacterium <NA>
## 3 Micrococcaceae Kocuria <NA>
## 4 Sphingobacteriaceae Sphingobacterium <NA>
## 5 Phyllobacteriaceae Mesorhizobium <NA>
## 6 Solirubrobacteraceae Solirubrobacter <NA>
## 7 Micromonosporaceae Asanoa <NA>
## 8 Comamonadaceae Acidovorax <NA>
## 9 Carnobacteriaceae Carnobacterium <NA>
## 10 Enterobacteriaceae Buttiauxella <NA>
## 11 Porphyromonadaceae Parabacteroides <NA>
## 12 Fusobacteriaceae Fusobacterium <NA>
## 13 Flavobacteriaceae Chryseobacterium <NA>
## 14 Iamiaceae Iamia <NA>
## 15 Propionibacteriaceae Mariniluteicoccus <NA>
## 16 Cytophagaceae Dyadobacter <NA>
## 17 Microbacteriaceae Agrococcus <NA>
## 18 Flavobacteriaceae Leeuwenhoekiella <NA>
## 19 Bradyrhizobiaceae Bosea <NA>
## 20 Bifidobacteriaceae Bifidobacterium <NA>
## 21 Hyphomicrobiaceae Hyphomicrobium <NA>
## 22 Comamonadaceae Delftia <NA>
## 23 Micromonosporaceae Actinoplanes <NA>
## 24 Veillonellaceae Pelosinus <NA>
## 25 Acidothermaceae Acidothermus <NA>
## 26 Rhizobiaceae Rhizobium <NA>
## 27 Caulobacteraceae Caulobacter <NA>
## 28 Prevotellaceae Prevotella <NA>
## 29 Nitrospiraceae Nitrospira <NA>
## 30 Micrococcaceae Rothia <NA>
## 31 Kallotenuaceae Kallotenue <NA>
## 32 Labilitrichaceae Labilithrix <NA>
## 33 Microbacteriaceae Leifsonia <NA>
## 34 Enterobacteriaceae Enterobacter <NA>
## 35 Burkholderiaceae Ralstonia <NA>
## 36 Rhodobacteraceae Amaricoccus <NA>
## 37 Microbacteriaceae Curtobacterium <NA>
## 38 Desulfovibrionaceae Desulfovibrio <NA>
## 39 Propionibacteriaceae Propionibacterium <NA>
## 40 Bacillaceae_1 Bacillus <NA>
## 41 Comamonadaceae Comamonas <NA>
## 42 Planctomycetaceae Gemmata <NA>
## 43 Iamiaceae Aquihabitans <NA>
## 44 Gemmatimonadaceae Gemmatimonas <NA>
## 45 Nocardioidaceae Nocardioides <NA>
## 46 Bacillales_Incertae_Sedis_XI Gemella <NA>
## 47 Enterococcaceae Enterococcus <NA>
## 48 Microbacteriaceae Pseudoclavibacter <NA>
## 49 Myxococcaceae Myxococcus <NA>
## 50 Rubrobacteraceae Rubrobacter <NA>
## 51 Coxiellaceae Diplorickettsia <NA>
## 52 Nocardiaceae Williamsia <NA>
## 53 Enterobacteriaceae Serratia <NA>
## 54 Sphingomonadaceae Sphingomonas <NA>
## 55 Propionibacteriaceae Microlunatus <NA>
## 56 Cytophagaceae Hymenobacter <NA>
## 57 Rhodobiaceae Methyloceanibacter <NA>
## 58 Nocardioidaceae Marmoricola <NA>
## 59 Bradyrhizobiaceae Bradyrhizobium <NA>
## 60 Streptococcaceae Streptococcus <NA>
## 61 Lactobacillaceae Lactobacillus <NA>
## 62 Sphingobacteriaceae Pedobacter <NA>
## 63 Shewanellaceae Shewanella <NA>
## 64 Brevibacteriaceae Brevibacterium <NA>
## 65 Eubacteriaceae Anaerofustis <NA>
## 66 Gaiellaceae Gaiella <NA>
## 67 Sphaerobacteraceae Sphaerobacter <NA>
## 68 Thermaceae Thermus <NA>
## 69 Pseudonocardiaceae Actinomycetospora <NA>
## 70 Beijerinckiaceae Beijerinckia <NA>
## 71 Lachnospiraceae Clostridium_XlVa <NA>
## 72 Micrococcaceae Micrococcus <NA>
## 73 Xanthomonadaceae Luteimonas <NA>
## 74 Sphingobacteriaceae Mucilaginibacter <NA>
## 75 Carnobacteriaceae Catellicoccus <NA>
## 76 Bradyrhizobiaceae Afipia <NA>
## 77 Rikenellaceae Alistipes <NA>
## 78 Peptostreptococcaceae Clostridium_XI <NA>
## 79 Patulibacteraceae Patulibacter <NA>
## 80 Streptomycetaceae Streptomyces <NA>
## 81 Tsukamurellaceae Tsukamurella <NA>
## 82 Propionibacteriaceae Friedmanniella <NA>
## 83 Lachnospiraceae Lactonifactor <NA>
## 84 Leuconostocaceae Weissella <NA>
## 85 Ruminococcaceae Ethanoligenens <NA>
## 86 Bacteroidaceae Bacteroides <NA>
## 87 Bacillaceae_1 Anaerobacillus <NA>
## 88 Dermabacteraceae Brachybacterium <NA>
## 89 Rhodobacteraceae Defluviimonas <NA>
## 90 Xanthomonadaceae Xanthomonas <NA>
## 91 Microbacteriaceae Amnibacterium <NA>
## 92 Cellulomonadaceae Cellulomonas <NA>
## 93 Dietziaceae Dietzia <NA>
## 94 Oxalobacteraceae Herbaspirillum <NA>
## 95 Conexibacteraceae Conexibacter <NA>
## 96 Alcaligenaceae Achromobacter <NA>
## 97 Rhizobiaceae Neorhizobium <NA>
## 98 Ruminococcaceae Anaerofilum <NA>
## 99 Oxalobacteraceae Massilia <NA>
## 100 Erysipelotrichaceae Turicibacter <NA>
## 101 Paenibacillaceae_1 Paenibacillus <NA>
## 102 Pseudonocardiaceae Pseudonocardia <NA>
## 103 Microbacteriaceae Microbacterium <NA>
## 104 Rhodobacteraceae Paracoccus <NA>
## 105 Flavobacteriaceae Flavobacterium <NA>
## 106 Methylobacteriaceae Microvirga <NA>
## 107 Sphingomonadaceae Novosphingobium <NA>
## 108 Kineosporiaceae Pseudokineococcus <NA>
## 109 Peptostreptococcaceae Romboutsia <NA>
## 110 Corynebacteriaceae Corynebacterium <NA>
## 111 Methylocystaceae Methylopila <NA>
## 112 Micrococcaceae Arthrobacter <NA>
## 113 Sphingomonadaceae Sphingobium <NA>
## 114 Geodermatophilaceae Geodermatophilus <NA>
## 115 Rhodobacteraceae Gemmobacter <NA>
## 116 Moraxellaceae Acinetobacter <NA>
## 117 Cellulomonadaceae Actinotalea <NA>
## 118 Deinococcaceae Deinococcus <NA>
## 119 Coriobacteriaceae Denitrobacterium <NA>
## 120 Veillonellaceae Veillonella <NA>
## 121 Hyphomicrobiaceae Rhodoplanes <NA>
## 122 Geodermatophilaceae Blastococcus <NA>
## 123 Pseudomonadaceae Cellvibrio <NA>
## 124 Acidimicrobineae_incertae_sedis Aciditerrimonas <NA>
## 125 Planococcaceae Lysinibacillus <NA>
## 126 Clostridiales_Incertae_Sedis_XIII Anaerovorax <NA>
## 127 Nocardiaceae Gordonia <NA>
## 128 Sanguibacteraceae Sanguibacter <NA>
## 129 Clostridiaceae_1 Clostridium_sensu_stricto <NA>
## 130 Ruminococcaceae Clostridium_IV <NA>
## 131 Staphylococcaceae Staphylococcus <NA>
## 132 Brucellaceae Ochrobactrum <NA>
## 133 Aurantimonadaceae Aureimonas <NA>
## 134 Rhizobiaceae Shinella <NA>
## 135 Sphaerobacteraceae Nitrolancea <NA>
## 136 Sphingomonadaceae Sphingopyxis <NA>
## 137 Mycobacteriaceae Mycobacterium <NA>
## 138 Nocardiaceae Rhodococcus <NA>
## 139 Lachnospiraceae Robinsoniella <NA>
## 140 Acidimicrobiaceae Ilumatobacter <NA>
## 141 Caulobacteraceae Brevundimonas <NA>
## 142 Intrasporangiaceae Janibacter <NA>
## 143 Burkholderiaceae Cupriavidus <NA>
## 144 Pseudomonadaceae Pseudomonas <NA>
## 145 Eubacteriaceae Eubacterium <NA>
## 146 Ruminococcaceae Intestinimonas <NA>
## 147 Xanthomonadaceae Stenotrophomonas <NA>
## 148 Hyphomicrobiaceae Pedomicrobium <NA>
## 149 Enterobacteriaceae Pantoea <NA>
## 150 Streptococcaceae Lactococcus <NA>
## 151 Alicyclobacillaceae Tumebacillus <NA>
## 152 Erysipelotrichaceae Clostridium_XVIII <NA>
## 153 Halomonadaceae Halomonas <NA>
## 154 Porphyromonadaceae Dysgonomonas <NA>res.liaG[res.liaG$pval.adj < 0.05,"Feature"]## [1] "ASV100" "ASV193" "ASV215" "ASV451" "ASV8"## Three genera sig, represented by these ASVs: "ASV193" "ASV215" "ASV301" "ASV451" "ASV8"
res.ds2G <- DA.ds2(TxO_G, predictor = "Type")## converting counts to integer mode## using pre-existing size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingres.ds2G## Feature baseMean log2FoldChange lfcSE stat pval
## 1 ASV10 33.55412340 -23.62467113 2.672456 -8.840058559 NA
## 2 ASV100 6.07683581 -2.52190026 1.487053 -1.695904368 0.0899039922
## 3 ASV1003 0.00000000 NA NA NA NA
## 4 ASV101 0.60411504 -2.84028417 3.053516 -0.930168329 0.3522839374
## 5 ASV1027 1.50832687 3.99003719 2.744948 1.453592969 0.1460591874
## 6 ASV103 18.95073219 1.99808881 1.557629 1.282776207 0.1995704923
## 7 ASV1046 0.46998886 1.53013308 3.061807 0.499748455 0.6172522090
## 8 ASV1067 0.35283526 -2.14917619 3.073489 -0.699262608 0.4843879305
## 9 ASV107 1.26149544 -3.77445638 2.324120 -1.624036656 0.1043679855
## 10 ASV11 56.62297621 3.56689114 2.109435 1.690922309 NA
## 11 ASV1108 0.21683770 -1.17537336 3.106011 -0.378418959 0.7051193865
## 12 ASV1122 0.20466753 -1.56705870 3.095046 -0.506311977 0.6126376565
## 13 ASV113 0.76873162 -3.15931448 2.569034 -1.229767462 0.2187841960
## 14 ASV1136 1.10468352 1.18524011 2.602815 0.455368488 0.6488441621
## 15 ASV1179 1.35230996 2.49351300 2.365409 1.054157445 0.2918108397
## 16 ASV1184 0.12173394 -1.06653766 3.108225 -0.343133999 0.7314976543
## 17 ASV121 3.08979618 -0.31083650 2.184897 -0.142265941 0.8868699477
## 18 ASV1211 0.17295254 -1.17537336 3.106011 -0.378418959 0.7051193865
## 19 ASV1214 0.21683770 -1.17537336 3.106011 -0.378418959 0.7051193865
## 20 ASV1241 0.00000000 NA NA NA NA
## 21 ASV1262 0.43768241 2.25421139 3.044547 0.740409457 0.4590515825
## 22 ASV129 2.26024130 -4.67806036 2.692111 -1.737691906 0.0822651156
## 23 ASV1318 1.14910298 3.61282560 2.762095 1.308002115 0.1908725921
## 24 ASV134 5.05210010 5.74769083 2.746038 2.093084956 NA
## 25 ASV1382 0.41231161 2.17517854 3.071743 0.708125119 0.4788675618
## 26 ASV139 2.04728484 -3.35090959 2.327969 -1.439413580 0.1500333789
## 27 ASV1397 0.00000000 NA NA NA NA
## 28 ASV1400 0.15350065 -1.30754222 3.102697 -0.421421194 0.6734475455
## 29 ASV1427 0.28755196 1.55532744 3.095982 0.502369592 0.6154075646
## 30 ASV1443 0.00000000 NA NA NA NA
## 31 ASV1470 0.40547437 1.22044087 2.869009 0.425387623 0.6705541274
## 32 ASV1473 0.20398846 1.36619389 3.101227 0.440533333 0.6595508758
## 33 ASV1485 0.00000000 NA NA NA NA
## 34 ASV15 8.65508133 -1.63064142 2.287311 -0.712907740 NA
## 35 ASV155 1.96379347 -4.47563828 2.733845 -1.637122177 NA
## 36 ASV1553 0.32198790 1.50677228 3.097272 0.486483760 0.6266242172
## 37 ASV170 2.82707436 0.01273622 1.850495 0.006882603 0.9945085210
## 38 ASV175 3.06257354 4.49227684 2.790339 1.609939218 NA
## 39 ASV176 1.42200689 -4.01349419 2.724610 -1.473052989 0.1407367483
## 40 ASV177 5.89378025 -0.83750977 1.861003 -0.450031299 0.6526878725
## 41 ASV1787 0.05116688 -0.55854955 3.116540 -0.179221064 0.8577641245
## 42 ASV1814 0.17905951 0.94581366 3.111658 0.303958127 0.7611597922
## 43 ASV1826 0.07056705 0.40323546 3.116540 0.129385636 0.8970525133
## 44 ASV1828 0.20466753 -1.56705870 3.095046 -0.506311977 0.6126376565
## 45 ASV183 15.66783720 -0.68903210 1.526307 -0.451437552 0.6516742234
## 46 ASV185 0.18588155 -1.48403724 3.097635 -0.479087193 0.6318766006
## 47 ASV19 64.93483725 1.28190755 2.036070 0.629599067 0.5289569342
## 48 ASV190 0.70861845 -3.01529528 2.611758 -1.154508042 0.2482919436
## 49 ASV1922 0.32019361 1.79860643 3.088549 0.582346725 0.5603331584
## 50 ASV193 8.02282816 4.90993848 1.690049 2.905204703 0.0036701298
## 51 ASV2 215.14279913 -9.79243542 2.936690 -3.334514748 NA
## 52 ASV204 1.46072281 0.04708302 2.381142 0.019773292 0.9842242236
## 53 ASV207 0.00000000 NA NA NA NA
## 54 ASV214 2.04860575 -4.54008057 2.176958 -2.085515701 0.0370225144
## 55 ASV215 6.42043205 4.98764221 1.700610 2.932855106 0.0033586059
## 56 ASV216 0.76904537 -3.06575485 2.892134 -1.060032183 0.2891299583
## 57 ASV2179 0.25913355 0.94581366 3.111658 0.303958127 0.7611597922
## 58 ASV2184 0.30209366 1.76348282 3.089919 0.570721319 0.5681885648
## 59 ASV221 2.81508052 0.10669045 2.215186 0.048163208 0.9615861717
## 60 ASV223 1.47253411 3.39950770 1.947233 1.745814119 0.0808432568
## 61 ASV226 1.27917209 -3.86321855 3.029787 -1.275079437 NA
## 62 ASV23 49.16573014 4.31248028 2.245197 1.920758423 0.0547621704
## 63 ASV235 1.26643399 -3.77166406 2.539712 -1.485075674 0.1375237791
## 64 ASV236 0.49930229 -2.59610435 2.996379 -0.866413957 0.3862631926
## 65 ASV237 0.00000000 NA NA NA NA
## 66 ASV248 1.56941284 4.05431746 2.496151 1.624227955 0.1043271666
## 67 ASV253 3.52299678 3.02042696 1.647518 1.833319060 0.0667551365
## 68 ASV256 2.51067751 2.19487574 2.619618 0.837860990 NA
## 69 ASV258 0.09294078 -0.94561239 3.110855 -0.303971881 0.7611493135
## 70 ASV26 23.64470916 -1.55336085 1.211976 -1.281676617 0.1999561109
## 71 ASV265 0.72801196 -3.06048199 2.871336 -1.065874038 0.2864805865
## 72 ASV267 5.34767146 2.61165806 2.163767 1.206996161 0.2274336225
## 73 ASV270 2.19078300 -4.63277137 2.729445 -1.697330731 NA
## 74 ASV273 1.42327975 -4.02008581 2.753689 -1.459891079 NA
## 75 ASV274 0.00000000 NA NA NA NA
## 76 ASV29 7.01618800 -0.56669258 2.665635 -0.212591961 0.8316452422
## 77 ASV291 1.00030527 -3.50798716 2.796793 -1.254289109 0.2097369409
## 78 ASV293 0.10841885 -0.55854955 3.116540 -0.179221064 0.8577641245
## 79 ASV296 0.32529271 -2.08537042 3.075833 -0.677985496 0.4977808897
## 80 ASV30 10.65626251 -1.63514014 1.633796 -1.000822812 0.3169124790
## 81 ASV303 1.83080235 2.87325870 2.081087 1.380652965 0.1673856897
## 82 ASV306 1.10149412 1.14435547 2.876145 0.397878171 0.6907199915
## 83 ASV31 15.32688333 -5.31449560 2.117787 -2.509457270 0.0120916844
## 84 ASV311 0.79228199 -3.09567508 2.889758 -1.071257372 0.2840537214
## 85 ASV312 0.40271925 -2.30184438 3.068263 -0.750211006 0.4531276310
## 86 ASV318 1.22294777 3.68742110 2.778546 1.327104644 0.1844740694
## 87 ASV332 4.70776089 1.94784693 1.753388 1.110904859 0.2666093039
## 88 ASV334 0.23235194 -1.70574993 3.090020 -0.552019137 0.5809352417
## 89 ASV337 2.33951637 4.62271165 2.510431 1.841401834 0.0655626943
## 90 ASV338 0.57548046 -2.56577902 2.957970 -0.867412257 0.3857161686
## 91 ASV342 0.00000000 NA NA NA NA
## 92 ASV348 0.97471505 -1.60451888 2.448577 -0.655286321 0.5122834262
## 93 ASV349 1.17152312 3.63085968 2.515186 1.443575080 0.1488585390
## 94 ASV356 0.17941705 -1.38617584 3.100374 -0.447099495 0.6548032234
## 95 ASV359 0.39347757 -2.29931122 3.068345 -0.749365250 0.4536370898
## 96 ASV375 4.90625748 -0.08482477 1.596327 -0.053137471 0.9576223758
## 97 ASV376 0.54863267 -0.78530491 2.792242 -0.281245245 0.7785223044
## 98 ASV378 0.10841885 -0.55854955 3.116540 -0.179221064 0.8577641245
## 99 ASV381 0.66950938 -0.52186129 2.724734 -0.191527456 0.8481123717
## 100 ASV387 0.00000000 NA NA NA NA
## 101 ASV388 0.00000000 NA NA NA NA
## 102 ASV416 1.02449546 3.45533054 2.565667 1.346757023 0.1780585005
## 103 ASV43 12.71567582 0.01201618 1.433067 0.008384939 0.9933098649
## 104 ASV44 8.23767838 0.64202831 1.323556 0.485078418 0.6276207205
## 105 ASV440 0.72115376 -1.61521785 2.722752 -0.593229856 0.5530273347
## 106 ASV445 0.25583442 -1.79864327 3.086891 -0.582671479 0.5601144769
## 107 ASV451 5.90628348 3.65113946 1.729364 2.111261617 0.0347498312
## 108 ASV467 0.00000000 NA NA NA NA
## 109 ASV472 0.92449802 3.30377379 2.551813 1.294676978 0.1954316850
## 110 ASV473 0.10841885 -0.55854955 3.116540 -0.179221064 0.8577641245
## 111 ASV474 0.00000000 NA NA NA NA
## 112 ASV475 0.25018774 -0.55854955 3.116540 -0.179221064 0.8577641245
## 113 ASV483 1.77429907 0.30463173 2.206667 0.138050641 0.8902003952
## 114 ASV506 0.13941116 -1.23437584 3.104868 -0.397561420 0.6909535036
## 115 ASV517 0.93275684 -3.31957710 2.836310 -1.170385657 0.2418458051
## 116 ASV520 0.34077079 -2.11414380 3.074764 -0.687579153 0.4917178400
## 117 ASV53 10.52674255 -6.88094710 2.087893 -3.295640955 0.0009819743
## 118 ASV531 0.64537655 -0.88137672 2.994516 -0.294330276 0.7685055400
## 119 ASV538 0.35816818 -2.19484096 3.071871 -0.714496506 0.4749202159
## 120 ASV54 12.46867021 3.24960170 2.463578 1.319057971 0.1871497326
## 121 ASV558 0.18588155 -1.48403724 3.097635 -0.479087193 0.6318766006
## 122 ASV562 1.19836372 3.67579873 2.511846 1.463385490 0.1433619269
## 123 ASV566 0.53825114 -2.68003332 2.960780 -0.905178057 0.3653710650
## 124 ASV572 0.38465370 -2.24626239 3.070105 -0.731656481 0.4643782649
## 125 ASV585 1.19843671 3.68804837 2.744251 1.343918035 0.1789748933
## 126 ASV586 0.29654831 1.76157347 3.089985 0.570091210 0.5686158369
## 127 ASV588 0.39582360 -1.51017165 3.079481 -0.490398057 0.6238522514
## 128 ASV593 1.61145020 0.63081817 2.419152 0.260760093 0.7942775214
## 129 ASV6 22.25277429 -3.50481711 1.912289 -1.832786275 0.0668343611
## 130 ASV60 5.88038644 -6.03922380 3.008825 -2.007170243 NA
## 131 ASV605 6.31639824 2.66121694 2.389258 1.113825569 0.2653540256
## 132 ASV62 5.99201632 -4.57886532 1.702738 -2.689119731 0.0071640719
## 133 ASV641 0.00000000 NA NA NA NA
## 134 ASV645 0.79215925 -1.04311146 2.446965 -0.426287933 0.6698980498
## 135 ASV647 0.00000000 NA NA NA NA
## 136 ASV652 2.78667212 4.88184545 2.209489 2.209490871 0.0271405167
## 137 ASV665 0.28131967 -1.85962272 3.029108 -0.613917518 0.5392698332
## 138 ASV68 11.08286584 0.54087845 1.454788 0.371791940 0.7100477654
## 139 ASV69 5.09131254 0.83820042 1.745861 0.480107161 0.6311511966
## 140 ASV716 0.24955295 0.46492272 3.111658 0.149413195 0.8812276001
## 141 ASV728 3.36222291 2.73542589 1.912133 1.430562549 0.1525556273
## 142 ASV776 0.30700130 -2.00861822 3.078785 -0.652406063 0.5141392542
## 143 ASV784 0.54435242 2.54789538 2.711277 0.939740042 0.3473509206
## 144 ASV785 0.17295254 -1.17537336 3.106011 -0.378418959 0.7051193865
## 145 ASV8 53.19112654 -4.91092856 1.665842 -2.948016059 0.0031982045
## 146 ASV809 0.40449864 2.07240475 3.076695 0.673581552 0.5005773852
## 147 ASV81 6.20097594 0.68576219 2.278270 0.301001238 0.7634135508
## 148 ASV82 3.26989507 -5.20517981 2.404469 -2.164794007 0.0304034640
## 149 ASV850 0.65466732 2.80924466 2.627681 1.069096655 0.2850261185
## 150 ASV87 10.75846614 2.10878625 1.799299 1.172004293 0.2411953326
## 151 ASV9 38.14244810 0.49638074 2.889078 0.171812882 NA
## 152 ASV906 0.79208884 3.05195736 2.865830 1.064947190 0.2868998284
## 153 ASV91 18.46404374 2.22048307 1.720378 1.290695159 0.1968094039
## 154 ASV95 2.59428813 -4.86090283 2.996436 -1.622227891 NA
## 155 ASV999 0.20224932 0.94581366 3.111658 0.303958127 0.7611597922
## ordering pval.adj Method Kingdom
## 1 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 2 CloacalSwab>Fecal 0.6663472 DESeq2 man. geoMeans (ds2) Bacteria
## 3 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 4 CloacalSwab>Fecal 0.8375052 DESeq2 man. geoMeans (ds2) Bacteria
## 5 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 6 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 7 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 8 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 9 CloacalSwab>Fecal 0.6921245 DESeq2 man. geoMeans (ds2) Bacteria
## 10 Fecal>CloacalSwab NA DESeq2 man. geoMeans (ds2) Bacteria
## 11 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 12 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 13 CloacalSwab>Fecal 0.7254423 DESeq2 man. geoMeans (ds2) Bacteria
## 14 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 15 Fecal>CloacalSwab 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 16 CloacalSwab>Fecal 0.8965898 DESeq2 man. geoMeans (ds2) Bacteria
## 17 CloacalSwab>Fecal 0.9341208 DESeq2 man. geoMeans (ds2) Bacteria
## 18 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 19 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 20 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 21 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 22 CloacalSwab>Fecal 0.6478378 DESeq2 man. geoMeans (ds2) Bacteria
## 23 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 24 Fecal>CloacalSwab NA DESeq2 man. geoMeans (ds2) Bacteria
## 25 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 26 CloacalSwab>Fecal 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 27 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 28 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 29 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 30 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 31 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 32 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 33 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 34 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 35 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 36 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 37 Fecal>CloacalSwab 0.9945085 DESeq2 man. geoMeans (ds2) Bacteria
## 38 Fecal>CloacalSwab NA DESeq2 man. geoMeans (ds2) Bacteria
## 39 CloacalSwab>Fecal 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 40 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 41 CloacalSwab>Fecal 0.9237460 DESeq2 man. geoMeans (ds2) Bacteria
## 42 Fecal>CloacalSwab 0.8965898 DESeq2 man. geoMeans (ds2) Bacteria
## 43 Fecal>CloacalSwab 0.9341208 DESeq2 man. geoMeans (ds2) Bacteria
## 44 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 45 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 46 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 47 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 48 CloacalSwab>Fecal 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 49 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 50 Fecal>CloacalSwab 0.1156091 DESeq2 man. geoMeans (ds2) Bacteria
## 51 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 52 Fecal>CloacalSwab 0.9945085 DESeq2 man. geoMeans (ds2) Bacteria
## 53 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 54 CloacalSwab>Fecal 0.4664837 DESeq2 man. geoMeans (ds2) Bacteria
## 55 Fecal>CloacalSwab 0.1156091 DESeq2 man. geoMeans (ds2) Bacteria
## 56 CloacalSwab>Fecal 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 57 Fecal>CloacalSwab 0.8965898 DESeq2 man. geoMeans (ds2) Bacteria
## 58 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 59 Fecal>CloacalSwab 0.9850395 DESeq2 man. geoMeans (ds2) Bacteria
## 60 Fecal>CloacalSwab 0.6478378 DESeq2 man. geoMeans (ds2) Bacteria
## 61 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 62 Fecal>CloacalSwab 0.6015092 DESeq2 man. geoMeans (ds2) Bacteria
## 63 CloacalSwab>Fecal 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 64 CloacalSwab>Fecal 0.8690922 DESeq2 man. geoMeans (ds2) Bacteria
## 65 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 66 Fecal>CloacalSwab 0.6921245 DESeq2 man. geoMeans (ds2) Bacteria
## 67 Fecal>CloacalSwab 0.6015092 DESeq2 man. geoMeans (ds2) Bacteria
## 68 Fecal>CloacalSwab NA DESeq2 man. geoMeans (ds2) Bacteria
## 69 CloacalSwab>Fecal 0.8965898 DESeq2 man. geoMeans (ds2) Bacteria
## 70 CloacalSwab>Fecal 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 71 CloacalSwab>Fecal 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 72 Fecal>CloacalSwab 0.7347855 DESeq2 man. geoMeans (ds2) Bacteria
## 73 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 74 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 75 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 76 CloacalSwab>Fecal 0.9237460 DESeq2 man. geoMeans (ds2) Bacteria
## 77 CloacalSwab>Fecal 0.7142393 DESeq2 man. geoMeans (ds2) Bacteria
## 78 CloacalSwab>Fecal 0.9237460 DESeq2 man. geoMeans (ds2) Bacteria
## 79 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 80 CloacalSwab>Fecal 0.7829602 DESeq2 man. geoMeans (ds2) Bacteria
## 81 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 82 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 83 CloacalSwab>Fecal 0.2539254 DESeq2 man. geoMeans (ds2) Bacteria
## 84 CloacalSwab>Fecal 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 85 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 86 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 87 Fecal>CloacalSwab 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 88 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 89 Fecal>CloacalSwab 0.6015092 DESeq2 man. geoMeans (ds2) Bacteria
## 90 CloacalSwab>Fecal 0.8690922 DESeq2 man. geoMeans (ds2) Bacteria
## 91 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 92 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 93 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 94 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 95 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 96 CloacalSwab>Fecal 0.9850395 DESeq2 man. geoMeans (ds2) Bacteria
## 97 CloacalSwab>Fecal 0.8999432 DESeq2 man. geoMeans (ds2) Bacteria
## 98 CloacalSwab>Fecal 0.9237460 DESeq2 man. geoMeans (ds2) Bacteria
## 99 CloacalSwab>Fecal 0.9237460 DESeq2 man. geoMeans (ds2) Bacteria
## 100 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 101 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 102 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 103 Fecal>CloacalSwab 0.9945085 DESeq2 man. geoMeans (ds2) Bacteria
## 104 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 105 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 106 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 107 Fecal>CloacalSwab 0.4664837 DESeq2 man. geoMeans (ds2) Bacteria
## 108 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 109 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 110 CloacalSwab>Fecal 0.9237460 DESeq2 man. geoMeans (ds2) Bacteria
## 111 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 112 CloacalSwab>Fecal 0.9237460 DESeq2 man. geoMeans (ds2) Bacteria
## 113 Fecal>CloacalSwab 0.9341208 DESeq2 man. geoMeans (ds2) Bacteria
## 114 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 115 CloacalSwab>Fecal 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 116 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 117 CloacalSwab>Fecal 0.1156091 DESeq2 man. geoMeans (ds2) Bacteria
## 118 CloacalSwab>Fecal 0.8965898 DESeq2 man. geoMeans (ds2) Bacteria
## 119 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 120 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 121 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 122 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 123 CloacalSwab>Fecal 0.8525325 DESeq2 man. geoMeans (ds2) Bacteria
## 124 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 125 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 126 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 127 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 128 Fecal>CloacalSwab 0.9098088 DESeq2 man. geoMeans (ds2) Bacteria
## 129 CloacalSwab>Fecal 0.6015092 DESeq2 man. geoMeans (ds2) Bacteria
## 130 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 131 Fecal>CloacalSwab 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 132 CloacalSwab>Fecal 0.1805346 DESeq2 man. geoMeans (ds2) Bacteria
## 133 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 134 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 135 <NA> NA DESeq2 man. geoMeans (ds2) Bacteria
## 136 Fecal>CloacalSwab 0.4664837 DESeq2 man. geoMeans (ds2) Bacteria
## 137 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 138 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 139 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 140 Fecal>CloacalSwab 0.9341208 DESeq2 man. geoMeans (ds2) Bacteria
## 141 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 142 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 143 Fecal>CloacalSwab 0.8375052 DESeq2 man. geoMeans (ds2) Bacteria
## 144 CloacalSwab>Fecal 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 145 CloacalSwab>Fecal 0.1156091 DESeq2 man. geoMeans (ds2) Bacteria
## 146 Fecal>CloacalSwab 0.8858022 DESeq2 man. geoMeans (ds2) Bacteria
## 147 Fecal>CloacalSwab 0.8965898 DESeq2 man. geoMeans (ds2) Bacteria
## 148 CloacalSwab>Fecal 0.4664837 DESeq2 man. geoMeans (ds2) Bacteria
## 149 Fecal>CloacalSwab 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 150 Fecal>CloacalSwab 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 151 Fecal>CloacalSwab NA DESeq2 man. geoMeans (ds2) Bacteria
## 152 Fecal>CloacalSwab 0.7353633 DESeq2 man. geoMeans (ds2) Bacteria
## 153 Fecal>CloacalSwab 0.6998464 DESeq2 man. geoMeans (ds2) Bacteria
## 154 CloacalSwab>Fecal NA DESeq2 man. geoMeans (ds2) Bacteria
## 155 Fecal>CloacalSwab 0.8965898 DESeq2 man. geoMeans (ds2) Bacteria
## Phylum Class Order
## 1 Proteobacteria Gammaproteobacteria Oceanospirillales
## 2 Proteobacteria Alphaproteobacteria Rhizobiales
## 3 Actinobacteria Actinobacteria Actinomycetales
## 4 Bacteroidetes Sphingobacteriia Sphingobacteriales
## 5 Proteobacteria Alphaproteobacteria Rhizobiales
## 6 Actinobacteria Actinobacteria Solirubrobacterales
## 7 Actinobacteria Actinobacteria Actinomycetales
## 8 Proteobacteria Betaproteobacteria Burkholderiales
## 9 Firmicutes Bacilli Lactobacillales
## 10 Proteobacteria Gammaproteobacteria Enterobacteriales
## 11 Bacteroidetes Bacteroidia Bacteroidales
## 12 Fusobacteria Fusobacteriia Fusobacteriales
## 13 Bacteroidetes Flavobacteriia Flavobacteriales
## 14 Actinobacteria Actinobacteria Acidimicrobiales
## 15 Actinobacteria Actinobacteria Actinomycetales
## 16 Bacteroidetes Cytophagia Cytophagales
## 17 Actinobacteria Actinobacteria Actinomycetales
## 18 Bacteroidetes Flavobacteriia Flavobacteriales
## 19 Proteobacteria Alphaproteobacteria Rhizobiales
## 20 Actinobacteria Actinobacteria Bifidobacteriales
## 21 Proteobacteria Alphaproteobacteria Rhizobiales
## 22 Proteobacteria Betaproteobacteria Burkholderiales
## 23 Actinobacteria Actinobacteria Actinomycetales
## 24 Firmicutes Negativicutes Selenomonadales
## 25 Actinobacteria Actinobacteria Actinomycetales
## 26 Proteobacteria Alphaproteobacteria Rhizobiales
## 27 Proteobacteria Alphaproteobacteria Caulobacterales
## 28 Bacteroidetes Bacteroidia Bacteroidales
## 29 Nitrospirae Nitrospira Nitrospirales
## 30 Actinobacteria Actinobacteria Actinomycetales
## 31 Chloroflexi Chloroflexia Kallotenuales
## 32 Proteobacteria Deltaproteobacteria Myxococcales
## 33 Actinobacteria Actinobacteria Actinomycetales
## 34 Proteobacteria Gammaproteobacteria Enterobacteriales
## 35 Proteobacteria Betaproteobacteria Burkholderiales
## 36 Proteobacteria Alphaproteobacteria Rhodobacterales
## 37 Actinobacteria Actinobacteria Actinomycetales
## 38 Proteobacteria Deltaproteobacteria Desulfovibrionales
## 39 Actinobacteria Actinobacteria Actinomycetales
## 40 Firmicutes Bacilli Bacillales
## 41 Proteobacteria Betaproteobacteria Burkholderiales
## 42 Planctomycetes Planctomycetia Planctomycetales
## 43 Actinobacteria Actinobacteria Acidimicrobiales
## 44 Gemmatimonadetes Gemmatimonadetes Gemmatimonadales
## 45 Actinobacteria Actinobacteria Actinomycetales
## 46 Firmicutes Bacilli Bacillales
## 47 Firmicutes Bacilli Lactobacillales
## 48 Actinobacteria Actinobacteria Actinomycetales
## 49 Proteobacteria Deltaproteobacteria Myxococcales
## 50 Actinobacteria Actinobacteria Rubrobacterales
## 51 Proteobacteria Gammaproteobacteria Legionellales
## 52 Actinobacteria Actinobacteria Actinomycetales
## 53 Proteobacteria Gammaproteobacteria Enterobacteriales
## 54 Proteobacteria Alphaproteobacteria Sphingomonadales
## 55 Actinobacteria Actinobacteria Actinomycetales
## 56 Bacteroidetes Cytophagia Cytophagales
## 57 Proteobacteria Alphaproteobacteria Rhizobiales
## 58 Firmicutes Clostridia Clostridiales
## 59 Actinobacteria Actinobacteria Actinomycetales
## 60 Proteobacteria Alphaproteobacteria Rhizobiales
## 61 Firmicutes Bacilli Lactobacillales
## 62 Firmicutes Bacilli Lactobacillales
## 63 Bacteroidetes Sphingobacteriia Sphingobacteriales
## 64 Proteobacteria Gammaproteobacteria Alteromonadales
## 65 Actinobacteria Actinobacteria Actinomycetales
## 66 Firmicutes Clostridia Clostridiales
## 67 Actinobacteria Actinobacteria Gaiellales
## 68 Chloroflexi Thermomicrobia Sphaerobacterales
## 69 Deinococcus-Thermus Deinococci Thermales
## 70 Actinobacteria Actinobacteria Actinomycetales
## 71 Proteobacteria Alphaproteobacteria Rhizobiales
## 72 Firmicutes Clostridia Clostridiales
## 73 Actinobacteria Actinobacteria Actinomycetales
## 74 Proteobacteria Gammaproteobacteria Xanthomonadales
## 75 Bacteroidetes Sphingobacteriia Sphingobacteriales
## 76 Firmicutes Bacilli Lactobacillales
## 77 Proteobacteria Alphaproteobacteria Rhizobiales
## 78 Bacteroidetes Bacteroidia Bacteroidales
## 79 Firmicutes Clostridia Clostridiales
## 80 Actinobacteria Actinobacteria Solirubrobacterales
## 81 Actinobacteria Actinobacteria Actinomycetales
## 82 Actinobacteria Actinobacteria Actinomycetales
## 83 Actinobacteria Actinobacteria Actinomycetales
## 84 Firmicutes Clostridia Clostridiales
## 85 Firmicutes Bacilli Lactobacillales
## 86 Firmicutes Clostridia Clostridiales
## 87 Bacteroidetes Bacteroidia Bacteroidales
## 88 Firmicutes Bacilli Bacillales
## 89 Actinobacteria Actinobacteria Actinomycetales
## 90 Proteobacteria Alphaproteobacteria Rhodobacterales
## 91 Proteobacteria Gammaproteobacteria Xanthomonadales
## 92 Actinobacteria Actinobacteria Actinomycetales
## 93 Actinobacteria Actinobacteria Actinomycetales
## 94 Actinobacteria Actinobacteria Actinomycetales
## 95 Proteobacteria Betaproteobacteria Burkholderiales
## 96 Actinobacteria Actinobacteria Solirubrobacterales
## 97 Proteobacteria Betaproteobacteria Burkholderiales
## 98 Proteobacteria Alphaproteobacteria Rhizobiales
## 99 Firmicutes Clostridia Clostridiales
## 100 Proteobacteria Betaproteobacteria Burkholderiales
## 101 Firmicutes Erysipelotrichia Erysipelotrichales
## 102 Firmicutes Bacilli Bacillales
## 103 Actinobacteria Actinobacteria Actinomycetales
## 104 Actinobacteria Actinobacteria Actinomycetales
## 105 Proteobacteria Alphaproteobacteria Rhodobacterales
## 106 Bacteroidetes Flavobacteriia Flavobacteriales
## 107 Proteobacteria Alphaproteobacteria Rhizobiales
## 108 Proteobacteria Alphaproteobacteria Sphingomonadales
## 109 Actinobacteria Actinobacteria Actinomycetales
## 110 Firmicutes Clostridia Clostridiales
## 111 Actinobacteria Actinobacteria Actinomycetales
## 112 Proteobacteria Alphaproteobacteria Rhizobiales
## 113 Actinobacteria Actinobacteria Actinomycetales
## 114 Proteobacteria Alphaproteobacteria Sphingomonadales
## 115 Actinobacteria Actinobacteria Actinomycetales
## 116 Proteobacteria Alphaproteobacteria Rhodobacterales
## 117 Proteobacteria Gammaproteobacteria Pseudomonadales
## 118 Actinobacteria Actinobacteria Actinomycetales
## 119 Deinococcus-Thermus Deinococci Deinococcales
## 120 Actinobacteria Actinobacteria Coriobacteriales
## 121 Firmicutes Negativicutes Selenomonadales
## 122 Proteobacteria Alphaproteobacteria Rhizobiales
## 123 Actinobacteria Actinobacteria Actinomycetales
## 124 Proteobacteria Gammaproteobacteria Pseudomonadales
## 125 Actinobacteria Actinobacteria Acidimicrobiales
## 126 Firmicutes Bacilli Bacillales
## 127 Firmicutes Clostridia Clostridiales
## 128 Actinobacteria Actinobacteria Actinomycetales
## 129 Actinobacteria Actinobacteria Actinomycetales
## 130 Firmicutes Clostridia Clostridiales
## 131 Firmicutes Clostridia Clostridiales
## 132 Firmicutes Bacilli Bacillales
## 133 Proteobacteria Alphaproteobacteria Rhizobiales
## 134 Proteobacteria Alphaproteobacteria Rhizobiales
## 135 Proteobacteria Alphaproteobacteria Rhizobiales
## 136 Chloroflexi Thermomicrobia Sphaerobacterales
## 137 Proteobacteria Alphaproteobacteria Sphingomonadales
## 138 Actinobacteria Actinobacteria Actinomycetales
## 139 Actinobacteria Actinobacteria Actinomycetales
## 140 Firmicutes Clostridia Clostridiales
## 141 Actinobacteria Actinobacteria Acidimicrobiales
## 142 Proteobacteria Alphaproteobacteria Caulobacterales
## 143 Actinobacteria Actinobacteria Actinomycetales
## 144 Proteobacteria Betaproteobacteria Burkholderiales
## 145 Proteobacteria Gammaproteobacteria Pseudomonadales
## 146 Firmicutes Clostridia Clostridiales
## 147 Firmicutes Clostridia Clostridiales
## 148 Proteobacteria Gammaproteobacteria Xanthomonadales
## 149 Proteobacteria Alphaproteobacteria Rhizobiales
## 150 Proteobacteria Gammaproteobacteria Enterobacteriales
## 151 Firmicutes Bacilli Lactobacillales
## 152 Firmicutes Bacilli Bacillales
## 153 Firmicutes Erysipelotrichia Erysipelotrichales
## 154 Proteobacteria Gammaproteobacteria Oceanospirillales
## 155 Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species
## 1 Halomonadaceae Salinicola <NA>
## 2 Methylobacteriaceae Methylobacterium <NA>
## 3 Micrococcaceae Kocuria <NA>
## 4 Sphingobacteriaceae Sphingobacterium <NA>
## 5 Phyllobacteriaceae Mesorhizobium <NA>
## 6 Solirubrobacteraceae Solirubrobacter <NA>
## 7 Micromonosporaceae Asanoa <NA>
## 8 Comamonadaceae Acidovorax <NA>
## 9 Carnobacteriaceae Carnobacterium <NA>
## 10 Enterobacteriaceae Buttiauxella <NA>
## 11 Porphyromonadaceae Parabacteroides <NA>
## 12 Fusobacteriaceae Fusobacterium <NA>
## 13 Flavobacteriaceae Chryseobacterium <NA>
## 14 Iamiaceae Iamia <NA>
## 15 Propionibacteriaceae Mariniluteicoccus <NA>
## 16 Cytophagaceae Dyadobacter <NA>
## 17 Microbacteriaceae Agrococcus <NA>
## 18 Flavobacteriaceae Leeuwenhoekiella <NA>
## 19 Bradyrhizobiaceae Bosea <NA>
## 20 Bifidobacteriaceae Bifidobacterium <NA>
## 21 Hyphomicrobiaceae Hyphomicrobium <NA>
## 22 Comamonadaceae Delftia <NA>
## 23 Micromonosporaceae Actinoplanes <NA>
## 24 Veillonellaceae Pelosinus <NA>
## 25 Acidothermaceae Acidothermus <NA>
## 26 Rhizobiaceae Rhizobium <NA>
## 27 Caulobacteraceae Caulobacter <NA>
## 28 Prevotellaceae Prevotella <NA>
## 29 Nitrospiraceae Nitrospira <NA>
## 30 Micrococcaceae Rothia <NA>
## 31 Kallotenuaceae Kallotenue <NA>
## 32 Labilitrichaceae Labilithrix <NA>
## 33 Microbacteriaceae Leifsonia <NA>
## 34 Enterobacteriaceae Enterobacter <NA>
## 35 Burkholderiaceae Ralstonia <NA>
## 36 Rhodobacteraceae Amaricoccus <NA>
## 37 Microbacteriaceae Curtobacterium <NA>
## 38 Desulfovibrionaceae Desulfovibrio <NA>
## 39 Propionibacteriaceae Propionibacterium <NA>
## 40 Bacillaceae_1 Bacillus <NA>
## 41 Comamonadaceae Comamonas <NA>
## 42 Planctomycetaceae Gemmata <NA>
## 43 Iamiaceae Aquihabitans <NA>
## 44 Gemmatimonadaceae Gemmatimonas <NA>
## 45 Nocardioidaceae Nocardioides <NA>
## 46 Bacillales_Incertae_Sedis_XI Gemella <NA>
## 47 Enterococcaceae Enterococcus <NA>
## 48 Microbacteriaceae Pseudoclavibacter <NA>
## 49 Myxococcaceae Myxococcus <NA>
## 50 Rubrobacteraceae Rubrobacter <NA>
## 51 Coxiellaceae Diplorickettsia <NA>
## 52 Nocardiaceae Williamsia <NA>
## 53 Enterobacteriaceae Serratia <NA>
## 54 Sphingomonadaceae Sphingomonas <NA>
## 55 Propionibacteriaceae Microlunatus <NA>
## 56 Cytophagaceae Hymenobacter <NA>
## 57 Rhodobiaceae Methyloceanibacter <NA>
## 58 Ruminococcaceae Acetanaerobacterium <NA>
## 59 Nocardioidaceae Marmoricola <NA>
## 60 Bradyrhizobiaceae Bradyrhizobium <NA>
## 61 Streptococcaceae Streptococcus <NA>
## 62 Lactobacillaceae Lactobacillus <NA>
## 63 Sphingobacteriaceae Pedobacter <NA>
## 64 Shewanellaceae Shewanella <NA>
## 65 Brevibacteriaceae Brevibacterium <NA>
## 66 Eubacteriaceae Anaerofustis <NA>
## 67 Gaiellaceae Gaiella <NA>
## 68 Sphaerobacteraceae Sphaerobacter <NA>
## 69 Thermaceae Thermus <NA>
## 70 Pseudonocardiaceae Actinomycetospora <NA>
## 71 Beijerinckiaceae Beijerinckia <NA>
## 72 Lachnospiraceae Clostridium_XlVa <NA>
## 73 Micrococcaceae Micrococcus <NA>
## 74 Xanthomonadaceae Luteimonas <NA>
## 75 Sphingobacteriaceae Mucilaginibacter <NA>
## 76 Carnobacteriaceae Catellicoccus <NA>
## 77 Bradyrhizobiaceae Afipia <NA>
## 78 Rikenellaceae Alistipes <NA>
## 79 Peptostreptococcaceae Clostridium_XI <NA>
## 80 Patulibacteraceae Patulibacter <NA>
## 81 Streptomycetaceae Streptomyces <NA>
## 82 Tsukamurellaceae Tsukamurella <NA>
## 83 Propionibacteriaceae Friedmanniella <NA>
## 84 Lachnospiraceae Lactonifactor <NA>
## 85 Leuconostocaceae Weissella <NA>
## 86 Ruminococcaceae Ethanoligenens <NA>
## 87 Bacteroidaceae Bacteroides <NA>
## 88 Bacillaceae_1 Anaerobacillus <NA>
## 89 Dermabacteraceae Brachybacterium <NA>
## 90 Rhodobacteraceae Defluviimonas <NA>
## 91 Xanthomonadaceae Xanthomonas <NA>
## 92 Microbacteriaceae Amnibacterium <NA>
## 93 Cellulomonadaceae Cellulomonas <NA>
## 94 Dietziaceae Dietzia <NA>
## 95 Oxalobacteraceae Herbaspirillum <NA>
## 96 Conexibacteraceae Conexibacter <NA>
## 97 Alcaligenaceae Achromobacter <NA>
## 98 Rhizobiaceae Neorhizobium <NA>
## 99 Ruminococcaceae Anaerofilum <NA>
## 100 Oxalobacteraceae Massilia <NA>
## 101 Erysipelotrichaceae Turicibacter <NA>
## 102 Paenibacillaceae_1 Paenibacillus <NA>
## 103 Pseudonocardiaceae Pseudonocardia <NA>
## 104 Microbacteriaceae Microbacterium <NA>
## 105 Rhodobacteraceae Paracoccus <NA>
## 106 Flavobacteriaceae Flavobacterium <NA>
## 107 Methylobacteriaceae Microvirga <NA>
## 108 Sphingomonadaceae Novosphingobium <NA>
## 109 Kineosporiaceae Pseudokineococcus <NA>
## 110 Peptostreptococcaceae Romboutsia <NA>
## 111 Corynebacteriaceae Corynebacterium <NA>
## 112 Methylocystaceae Methylopila <NA>
## 113 Micrococcaceae Arthrobacter <NA>
## 114 Sphingomonadaceae Sphingobium <NA>
## 115 Geodermatophilaceae Geodermatophilus <NA>
## 116 Rhodobacteraceae Gemmobacter <NA>
## 117 Moraxellaceae Acinetobacter <NA>
## 118 Cellulomonadaceae Actinotalea <NA>
## 119 Deinococcaceae Deinococcus <NA>
## 120 Coriobacteriaceae Denitrobacterium <NA>
## 121 Veillonellaceae Veillonella <NA>
## 122 Hyphomicrobiaceae Rhodoplanes <NA>
## 123 Geodermatophilaceae Blastococcus <NA>
## 124 Pseudomonadaceae Cellvibrio <NA>
## 125 Acidimicrobineae_incertae_sedis Aciditerrimonas <NA>
## 126 Planococcaceae Lysinibacillus <NA>
## 127 Clostridiales_Incertae_Sedis_XIII Anaerovorax <NA>
## 128 Nocardiaceae Gordonia <NA>
## 129 Sanguibacteraceae Sanguibacter <NA>
## 130 Clostridiaceae_1 Clostridium_sensu_stricto <NA>
## 131 Ruminococcaceae Clostridium_IV <NA>
## 132 Staphylococcaceae Staphylococcus <NA>
## 133 Brucellaceae Ochrobactrum <NA>
## 134 Aurantimonadaceae Aureimonas <NA>
## 135 Rhizobiaceae Shinella <NA>
## 136 Sphaerobacteraceae Nitrolancea <NA>
## 137 Sphingomonadaceae Sphingopyxis <NA>
## 138 Mycobacteriaceae Mycobacterium <NA>
## 139 Nocardiaceae Rhodococcus <NA>
## 140 Lachnospiraceae Robinsoniella <NA>
## 141 Acidimicrobiaceae Ilumatobacter <NA>
## 142 Caulobacteraceae Brevundimonas <NA>
## 143 Intrasporangiaceae Janibacter <NA>
## 144 Burkholderiaceae Cupriavidus <NA>
## 145 Pseudomonadaceae Pseudomonas <NA>
## 146 Eubacteriaceae Eubacterium <NA>
## 147 Ruminococcaceae Intestinimonas <NA>
## 148 Xanthomonadaceae Stenotrophomonas <NA>
## 149 Hyphomicrobiaceae Pedomicrobium <NA>
## 150 Enterobacteriaceae Pantoea <NA>
## 151 Streptococcaceae Lactococcus <NA>
## 152 Alicyclobacillaceae Tumebacillus <NA>
## 153 Erysipelotrichaceae Clostridium_XVIII <NA>
## 154 Halomonadaceae Halomonas <NA>
## 155 Porphyromonadaceae Dysgonomonas <NA>res.ds2G[res.ds2G$pval.adj < 0.05,"Feature"]## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [24] NA NA NA NA NA NA## Nothing with ds2...hmmm...
featurePlot(TxO_G, predictor = "Type", feature = "ASV8") + theme_classic()
featurePlot(TxO_G, predictor = "Type", feature = "ASV193") + theme_classic()
featurePlot(TxO_G, predictor = "Type", feature = "ASV215") + theme_classic()
## These plots do show differences between sample types at the genus level. Would report
## last phylum level
TxO_P <- tax_glom(TxO, taxrank = "Phylum")
TxO_P## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 13 taxa and 12 samples ]
## sample_data() Sample Data: [ 12 samples by 21 sample variables ]
## tax_table() Taxonomy Table: [ 13 taxa by 7 taxonomic ranks ]
## refseq() DNAStringSet: [ 13 reference sequences ]## Testing 13 phyla
res.liaP <- DA.lia(TxO_P, predictor = "Type")## Warning: Zero sample variances detected, have been offset away from zerores.liaP## Feature logFC AveExpr t pval pval.adj
## 1 ASV1122 0.0000000 0.0000000 0.0000000 1.000000000 1.00000000
## 2 ASV1133 1.4664121 0.5018161 3.2956415 0.007470802 0.06052890
## 3 ASV1427 0.3446892 -0.0578554 1.3740281 0.197759349 0.33901603
## 4 ASV1814 0.2291647 -0.1156177 1.0203199 0.330275591 0.43965824
## 5 ASV2 -1.0178976 5.3144260 -2.1434962 0.056155269 0.16846581
## 6 ASV24 0.2553467 3.3587654 0.2477682 0.809042583 0.88259191
## 7 ASV256 2.1045247 1.3199905 2.8663987 0.015858915 0.06343566
## 8 ASV332 -0.7954141 1.6431743 -1.1872599 0.261023085 0.39153463
## 9 ASV538 -0.2077745 0.1038872 -1.5919540 0.140720644 0.28144129
## 10 ASV6 0.3843566 5.3276197 0.9435603 0.366381870 0.43965824
## 11 ASV806 1.1104825 0.6226485 1.9395357 0.079470144 0.19072835
## 12 ASV9 1.5148381 5.0950255 3.1237948 0.010088150 0.06052890
## B ordering Method Kingdom
## 1 -5.958500 <NA> LIMMA - ALR (lia) Bacteria
## 2 -2.242701 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 3 -5.075024 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 4 -5.452306 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 5 -4.032037 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 6 -5.927207 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 7 -2.916452 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 8 -5.284627 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 9 -4.805133 CloacalSwab>Fecal LIMMA - ALR (lia) Bacteria
## 10 -5.522540 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 11 -4.329969 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## 12 -2.511772 Fecal>CloacalSwab LIMMA - ALR (lia) Bacteria
## Phylum Class Order Family Genus Species
## 1 Fusobacteria <NA> <NA> <NA> <NA> <NA>
## 2 Acidobacteria <NA> <NA> <NA> <NA> <NA>
## 3 Nitrospirae <NA> <NA> <NA> <NA> <NA>
## 4 Planctomycetes <NA> <NA> <NA> <NA> <NA>
## 5 Proteobacteria <NA> <NA> <NA> <NA> <NA>
## 6 Cyanobacteria/Chloroplast <NA> <NA> <NA> <NA> <NA>
## 7 Chloroflexi <NA> <NA> <NA> <NA> <NA>
## 8 Bacteroidetes <NA> <NA> <NA> <NA> <NA>
## 9 Deinococcus-Thermus <NA> <NA> <NA> <NA> <NA>
## 10 Actinobacteria <NA> <NA> <NA> <NA> <NA>
## 11 Verrucomicrobia <NA> <NA> <NA> <NA> <NA>
## 12 Firmicutes <NA> <NA> <NA> <NA> <NA>res.liaP[res.liaP$pval.adj < 0.05,"Feature"]## character(0)## None here, but you can see that a few phyla are close to sig at 0.06
res.ds2P <- DA.ds2(TxO_P, predictor = "Type")## converting counts to integer mode## using pre-existing size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingres.ds2P## Feature baseMean log2FoldChange lfcSE stat pval
## 1 ASV1122 0.2162640 -1.5094068 3.1010080 -0.4867472 0.62643752
## 2 ASV1133 2.1332349 3.9165449 1.5677152 2.4982503 0.01248080
## 3 ASV1427 0.1896535 1.2721482 3.1114549 0.4088596 0.68264270
## 4 ASV1814 0.1296656 0.9280411 3.1165397 0.2977793 0.76587160
## 5 ASV1828 0.2162640 -1.5094068 3.1010080 -0.4867472 0.62643752
## 6 ASV2 404.0850186 -1.6166406 0.8160714 -1.9810037 0.04759086
## 7 ASV24 84.6812366 0.2937638 1.0777373 0.2725746 0.78518027
## 8 ASV256 10.0769723 2.1914054 1.6407099 1.3356447 0.18166543
## 9 ASV332 9.7430269 -0.6174584 0.9759300 -0.6326871 0.52693796
## 10 ASV538 0.5203201 -2.5748290 2.6990589 -0.9539729 0.34009735
## 11 ASV6 256.4355836 0.2158250 0.5952578 0.3625741 0.71692307
## 12 ASV806 2.5089056 2.1984062 1.4710593 1.4944376 0.13506129
## 13 ASV9 266.7259972 1.4063539 0.7236906 1.9433082 0.05197893
## ordering pval.adj Method Kingdom
## 1 CloacalSwab>Fecal 0.7851803 DESeq2 man. geoMeans (ds2) Bacteria
## 2 Fecal>CloacalSwab 0.1622505 DESeq2 man. geoMeans (ds2) Bacteria
## 3 Fecal>CloacalSwab 0.7851803 DESeq2 man. geoMeans (ds2) Bacteria
## 4 Fecal>CloacalSwab 0.7851803 DESeq2 man. geoMeans (ds2) Bacteria
## 5 CloacalSwab>Fecal 0.7851803 DESeq2 man. geoMeans (ds2) Bacteria
## 6 CloacalSwab>Fecal 0.2252420 DESeq2 man. geoMeans (ds2) Bacteria
## 7 Fecal>CloacalSwab 0.7851803 DESeq2 man. geoMeans (ds2) Bacteria
## 8 Fecal>CloacalSwab 0.4723301 DESeq2 man. geoMeans (ds2) Bacteria
## 9 CloacalSwab>Fecal 0.7851803 DESeq2 man. geoMeans (ds2) Bacteria
## 10 CloacalSwab>Fecal 0.7368776 DESeq2 man. geoMeans (ds2) Bacteria
## 11 Fecal>CloacalSwab 0.7851803 DESeq2 man. geoMeans (ds2) Bacteria
## 12 Fecal>CloacalSwab 0.4389492 DESeq2 man. geoMeans (ds2) Bacteria
## 13 Fecal>CloacalSwab 0.2252420 DESeq2 man. geoMeans (ds2) Bacteria
## Phylum Class Order Family Genus Species
## 1 Fusobacteria <NA> <NA> <NA> <NA> <NA>
## 2 Acidobacteria <NA> <NA> <NA> <NA> <NA>
## 3 Nitrospirae <NA> <NA> <NA> <NA> <NA>
## 4 Planctomycetes <NA> <NA> <NA> <NA> <NA>
## 5 Gemmatimonadetes <NA> <NA> <NA> <NA> <NA>
## 6 Proteobacteria <NA> <NA> <NA> <NA> <NA>
## 7 Cyanobacteria/Chloroplast <NA> <NA> <NA> <NA> <NA>
## 8 Chloroflexi <NA> <NA> <NA> <NA> <NA>
## 9 Bacteroidetes <NA> <NA> <NA> <NA> <NA>
## 10 Deinococcus-Thermus <NA> <NA> <NA> <NA> <NA>
## 11 Actinobacteria <NA> <NA> <NA> <NA> <NA>
## 12 Verrucomicrobia <NA> <NA> <NA> <NA> <NA>
## 13 Firmicutes <NA> <NA> <NA> <NA> <NA>res.ds2P[res.ds2P$pval.adj < 0.05,"Feature"]## character(0)## Can look at plot again. Surprisingly Proteobacteria nor Bacteroidetes come back as signifant (or near sig) in either of the tests even though they look quite different here. May reflect low sample size...
ggplot(data=Tx_phylum, aes(x=Sample, y=Abundance, fill=Phylum)) +geom_bar(aes(), stat="identity", position="stack") +
ylab("Relative abundance (% of sequences)") +theme_classic() + theme_bw() +
theme_classic()+ labs(x = "")+ theme(text = element_text(size = 18)) + scale_fill_manual(values=cbPalette,name="Phylum") + theme(axis.text.x = element_text(angle = 50, vjust = 0.5))
Indicator Species analysis
######## Indicator Species Analysis ##########
## Run this on the same dataset did for DA testing
## Looking for taxa that distinguish groups. In differential abundance testing above the bacterial taxa may be present in both groups, but the abundance is difference. In the ISA below we are looking for taxa that are generally in one group and not the other.
TxODA## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 133 taxa and 12 samples ]
## sample_data() Sample Data: [ 12 samples by 21 sample variables ]
## tax_table() Taxonomy Table: [ 133 taxa by 7 taxonomic ranks ]## 133 taxa found in at least 3 samples
dfTxO_R <- as(sample_data(TxODA), "data.frame")
otu_R <- as.data.frame(t(as(otu_table(TxODA), "matrix")))
### On presence/absence data instead
otu_pa = as.data.frame(ifelse(otu_R>0,1,0))
library(indicspecies)##
## Attaching package: 'indicspecies'## The following object is masked from 'package:Biostrings':
##
## coverage## The following object is masked from 'package:IRanges':
##
## coverageset.seed(714)
## Think about max.order depending on the number of groups you have
indval_pa = multipatt(otu_pa, dfTxO_R$Type, max.order =2, control = how(nperm=999))
summary(indval_pa, indvalcomp = T)##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: IndVal.g
## Significance level (alpha): 0.05
##
## Total number of species: 133
## Selected number of species: 10
## Number of species associated to 1 group: 10
##
## List of species associated to each combination:
##
## Group CloacalSwab #sps. 3
## A B stat p.value
## ASV20 1.0000 1.0000 1.000 0.001 ***
## ASV6 0.8571 1.0000 0.926 0.011 *
## ASV62 1.0000 0.8333 0.913 0.013 *
##
## Group Fecal #sps. 7
## A B stat p.value
## ASV114 1.0000 1.0000 1.000 0.001 ***
## ASV193 1.0000 1.0000 1.000 0.001 ***
## ASV301 1.0000 1.0000 1.000 0.001 ***
## ASV302 1.0000 1.0000 1.000 0.001 ***
## ASV103 0.8571 1.0000 0.926 0.016 *
## ASV65 1.0000 0.8333 0.913 0.015 *
## ASV451 1.0000 0.8333 0.913 0.011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## BUT you need to correct for multiple comparisons
indval_pa$sign## s.CloacalSwab s.Fecal index stat p.value
## ASV6 1 0 1 0.9258201 0.011
## ASV8 1 0 1 0.8164966 0.054
## ASV10 1 0 1 0.7071068 0.171
## ASV11 1 0 1 0.7715167 0.250
## ASV12 1 0 1 0.8164966 0.054
## ASV15 1 1 3 0.6454972 NA
## ASV20 1 0 1 1.0000000 0.001
## ASV22 1 1 3 0.8164966 NA
## ASV26 1 0 1 0.7302967 0.240
## ASV28 1 0 1 0.7071068 0.187
## ASV29 1 1 3 0.5000000 NA
## ASV30 1 1 3 0.8660254 NA
## ASV31 1 0 1 0.7302967 0.232
## ASV34 1 0 1 0.7071068 0.187
## ASV38 1 0 1 0.7071068 0.171
## ASV40 1 0 1 0.8164966 0.058
## ASV43 1 1 3 0.7637626 NA
## ASV44 1 1 3 0.7071068 NA
## ASV47 1 1 3 0.7071068 NA
## ASV53 1 0 1 0.7071068 0.187
## ASV54 0 1 2 0.6123724 0.559
## ASV61 1 1 3 0.5773503 NA
## ASV62 1 0 1 0.9128709 0.013
## ASV65 0 1 2 0.9128709 0.015
## ASV68 1 1 3 0.8164966 NA
## ASV69 1 1 3 0.8164966 NA
## ASV70 1 0 1 0.7302967 0.255
## ASV72 1 1 3 0.7071068 NA
## ASV74 1 1 3 0.5773503 NA
## ASV76 1 0 1 0.8164966 0.064
## ASV81 0 1 2 0.6123724 0.551
## ASV87 1 1 3 0.5773503 NA
## ASV90 1 1 3 0.5000000 NA
## ASV91 0 1 2 0.6123724 0.552
## ASV97 0 1 2 0.6123724 0.552
## ASV100 1 1 3 0.6454972 NA
## ASV103 0 1 2 0.9258201 0.016
## ASV105 0 1 2 0.7715167 0.234
## ASV107 1 0 1 0.8164966 0.054
## ASV114 0 1 2 1.0000000 0.001
## ASV121 1 1 3 0.6454972 NA
## ASV123 1 1 3 0.5000000 NA
## ASV124 1 1 3 0.5000000 NA
## ASV132 1 1 3 0.5000000 NA
## ASV133 1 1 3 0.5000000 NA
## ASV135 1 1 3 0.5773503 NA
## ASV137 1 1 3 0.6454972 NA
## ASV151 1 1 3 0.7071068 NA
## ASV158 1 1 3 0.5773503 NA
## ASV159 1 1 3 0.7071068 NA
## ASV167 1 1 3 0.6454972 NA
## ASV170 1 1 3 0.6454972 NA
## ASV174 1 0 1 0.8333333 0.081
## ASV178 0 1 2 0.6123724 0.542
## ASV183 1 1 3 0.6454972 NA
## ASV190 1 0 1 0.7071068 0.171
## ASV192 0 1 2 0.8333333 0.073
## ASV193 0 1 2 1.0000000 0.001
## ASV194 1 1 3 0.5000000 NA
## ASV196 1 1 3 0.5000000 NA
## ASV199 0 1 2 0.7715167 0.219
## ASV201 1 1 3 0.5000000 NA
## ASV203 1 1 3 0.5000000 NA
## ASV209 0 1 2 0.6123724 0.522
## ASV210 0 1 2 0.6123724 0.529
## ASV215 0 1 2 0.7302967 0.213
## ASV219 0 1 2 0.6123724 0.534
## ASV223 0 1 2 0.8333333 0.075
## ASV232 1 1 3 0.5000000 NA
## ASV238 1 1 3 0.5000000 NA
## ASV241 1 0 1 0.6123724 0.550
## ASV242 1 1 3 0.5000000 NA
## ASV245 0 1 2 0.6123724 0.559
## ASV248 0 1 2 0.7071068 0.182
## ASV250 0 1 2 0.6123724 0.542
## ASV253 0 1 2 0.7302967 0.244
## ASV256 1 1 3 0.5000000 NA
## ASV260 0 1 2 0.7071068 0.182
## ASV264 1 1 3 0.5000000 NA
## ASV267 0 1 2 0.7071068 0.199
## ASV268 0 1 2 0.6123724 0.552
## ASV278 0 1 2 0.7071068 0.182
## ASV281 0 1 2 0.8164966 0.059
## ASV301 0 1 2 1.0000000 0.001
## ASV302 0 1 2 1.0000000 0.001
## ASV303 1 1 3 0.6454972 NA
## ASV309 0 1 2 0.8164966 0.057
## ASV319 0 1 2 0.7302967 0.242
## ASV326 1 1 3 0.5000000 NA
## ASV331 0 1 2 0.6123724 0.549
## ASV332 1 1 3 0.5000000 NA
## ASV340 1 1 3 0.5000000 NA
## ASV343 1 0 1 0.6123724 0.555
## ASV349 0 1 2 0.7071068 0.190
## ASV361 1 0 1 0.6123724 0.531
## ASV375 0 1 2 0.8164966 0.057
## ASV376 1 1 3 0.5000000 NA
## ASV381 1 1 3 0.5000000 NA
## ASV403 0 1 2 0.8164966 0.057
## ASV408 0 1 2 0.6123724 0.549
## ASV451 0 1 2 0.9128709 0.011
## ASV472 0 1 2 0.7071068 0.199
## ASV480 1 1 3 0.5000000 NA
## ASV482 0 1 2 0.8164966 0.057
## ASV483 1 1 3 0.5000000 NA
## ASV489 0 1 2 0.7071068 0.192
## ASV562 0 1 2 0.7071068 0.192
## ASV570 1 1 3 0.5000000 NA
## ASV591 0 1 2 0.8164966 0.056
## ASV665 1 0 1 0.7071068 0.187
## ASV672 0 1 2 0.7071068 0.182
## ASV728 0 1 2 0.8164966 0.064
## ASV759 1 1 3 0.5000000 NA
## ASV762 1 1 3 0.5000000 NA
## ASV784 0 1 2 0.7071068 0.182
## ASV806 0 1 2 0.7071068 0.172
## ASV808 0 1 2 0.7071068 0.175
## ASV819 0 1 2 0.7071068 0.198
## ASV832 0 1 2 0.8164966 0.057
## ASV835 1 1 3 0.5000000 NA
## ASV850 0 1 2 0.7071068 0.172
## ASV851 1 1 3 0.5000000 NA
## ASV884 0 1 2 0.7071068 0.192
## ASV925 1 1 3 0.5000000 NA
## ASV959 0 1 2 0.8164966 0.057
## ASV1107 0 1 2 0.8164966 0.065
## ASV1133 1 1 3 0.5000000 NA
## ASV1155 0 1 2 0.7071068 0.184
## ASV1156 1 1 3 0.5000000 NA
## ASV1180 0 1 2 0.7071068 0.163
## ASV1231 0 1 2 0.7071068 0.193
## ASV1470 1 1 3 0.5000000 NA
## Others 1 1 3 1.0000000 NA##A -->
# this is the species that only occur in one group, but may not occur in all the replicates
# Faithful to group analysis (B)
# this is the species that occur only in that group and in all replicates
##basically A and B have to both be above 0.7
p_2 <- indval_pa$sign$p.value
## taking p values from that and making it a vector of just p values
p_adj_2 <- round(p.adjust(p_2, "fdr"),3)
## adjusting p values to do multiple comparisons,fdr --> false discovery rate
cbind(p_adj_2,rownames(indval_pa$sign))## p_adj_2
## [1,] "0.123" "ASV6"
## [2,] "0.203" "ASV8"
## [3,] "0.293" "ASV10"
## [4,] "0.32" "ASV11"
## [5,] "0.203" "ASV12"
## [6,] NA "ASV15"
## [7,] "0.016" "ASV20"
## [8,] NA "ASV22"
## [9,] "0.317" "ASV26"
## [10,] "0.293" "ASV28"
## [11,] NA "ASV29"
## [12,] NA "ASV30"
## [13,] "0.317" "ASV31"
## [14,] "0.293" "ASV34"
## [15,] "0.293" "ASV38"
## [16,] "0.203" "ASV40"
## [17,] NA "ASV43"
## [18,] NA "ASV44"
## [19,] NA "ASV47"
## [20,] "0.293" "ASV53"
## [21,] "0.559" "ASV54"
## [22,] NA "ASV61"
## [23,] "0.125" "ASV62"
## [24,] "0.125" "ASV65"
## [25,] NA "ASV68"
## [26,] NA "ASV69"
## [27,] "0.321" "ASV70"
## [28,] NA "ASV72"
## [29,] NA "ASV74"
## [30,] "0.203" "ASV76"
## [31,] "0.559" "ASV81"
## [32,] NA "ASV87"
## [33,] NA "ASV90"
## [34,] "0.559" "ASV91"
## [35,] "0.559" "ASV97"
## [36,] NA "ASV100"
## [37,] "0.125" "ASV103"
## [38,] "0.317" "ASV105"
## [39,] "0.203" "ASV107"
## [40,] "0.016" "ASV114"
## [41,] NA "ASV121"
## [42,] NA "ASV123"
## [43,] NA "ASV124"
## [44,] NA "ASV132"
## [45,] NA "ASV133"
## [46,] NA "ASV135"
## [47,] NA "ASV137"
## [48,] NA "ASV151"
## [49,] NA "ASV158"
## [50,] NA "ASV159"
## [51,] NA "ASV167"
## [52,] NA "ASV170"
## [53,] "0.226" "ASV174"
## [54,] "0.559" "ASV178"
## [55,] NA "ASV183"
## [56,] "0.293" "ASV190"
## [57,] "0.217" "ASV192"
## [58,] "0.016" "ASV193"
## [59,] NA "ASV194"
## [60,] NA "ASV196"
## [61,] "0.311" "ASV199"
## [62,] NA "ASV201"
## [63,] NA "ASV203"
## [64,] "0.559" "ASV209"
## [65,] "0.559" "ASV210"
## [66,] "0.308" "ASV215"
## [67,] "0.559" "ASV219"
## [68,] "0.217" "ASV223"
## [69,] NA "ASV232"
## [70,] NA "ASV238"
## [71,] "0.559" "ASV241"
## [72,] NA "ASV242"
## [73,] "0.559" "ASV245"
## [74,] "0.293" "ASV248"
## [75,] "0.559" "ASV250"
## [76,] "0.317" "ASV253"
## [77,] NA "ASV256"
## [78,] "0.293" "ASV260"
## [79,] NA "ASV264"
## [80,] "0.293" "ASV267"
## [81,] "0.559" "ASV268"
## [82,] "0.293" "ASV278"
## [83,] "0.203" "ASV281"
## [84,] "0.016" "ASV301"
## [85,] "0.016" "ASV302"
## [86,] NA "ASV303"
## [87,] "0.203" "ASV309"
## [88,] "0.317" "ASV319"
## [89,] NA "ASV326"
## [90,] "0.559" "ASV331"
## [91,] NA "ASV332"
## [92,] NA "ASV340"
## [93,] "0.559" "ASV343"
## [94,] "0.293" "ASV349"
## [95,] "0.559" "ASV361"
## [96,] "0.203" "ASV375"
## [97,] NA "ASV376"
## [98,] NA "ASV381"
## [99,] "0.203" "ASV403"
## [100,] "0.559" "ASV408"
## [101,] "0.123" "ASV451"
## [102,] "0.293" "ASV472"
## [103,] NA "ASV480"
## [104,] "0.203" "ASV482"
## [105,] NA "ASV483"
## [106,] "0.293" "ASV489"
## [107,] "0.293" "ASV562"
## [108,] NA "ASV570"
## [109,] "0.203" "ASV591"
## [110,] "0.293" "ASV665"
## [111,] "0.293" "ASV672"
## [112,] "0.203" "ASV728"
## [113,] NA "ASV759"
## [114,] NA "ASV762"
## [115,] "0.293" "ASV784"
## [116,] "0.293" "ASV806"
## [117,] "0.293" "ASV808"
## [118,] "0.293" "ASV819"
## [119,] "0.203" "ASV832"
## [120,] NA "ASV835"
## [121,] "0.293" "ASV850"
## [122,] NA "ASV851"
## [123,] "0.293" "ASV884"
## [124,] NA "ASV925"
## [125,] "0.203" "ASV959"
## [126,] "0.203" "ASV1107"
## [127,] NA "ASV1133"
## [128,] "0.293" "ASV1155"
## [129,] NA "ASV1156"
## [130,] "0.293" "ASV1180"
## [131,] "0.293" "ASV1231"
## [132,] NA "ASV1470"
## [133,] NA "Others"table_ISA <- as.data.frame(cbind(p_adj_2,rownames(indval_pa$sign)))
## going to write this table, because using permutations values can change.
write.csv(table_ISA, "ASVs_from_indicator_species.csv")
#my_sigASVs <- read.csv("ASVs_from_indicator_species.csv", header = T, stringsAsFactors = F)
my_sigASVs <- table_ISA[ which(p_adj_2 < 0.05 ),]
my_sigASVs <- my_sigASVs[,2]
## Five ASVs, now let's make sure they had good indicator values
## ASV20 ASV114 ASV193 ASV301 ASV302
summary(indval_pa, indvalcomp = T)##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: IndVal.g
## Significance level (alpha): 0.05
##
## Total number of species: 133
## Selected number of species: 10
## Number of species associated to 1 group: 10
##
## List of species associated to each combination:
##
## Group CloacalSwab #sps. 3
## A B stat p.value
## ASV20 1.0000 1.0000 1.000 0.001 ***
## ASV6 0.8571 1.0000 0.926 0.011 *
## ASV62 1.0000 0.8333 0.913 0.013 *
##
## Group Fecal #sps. 7
## A B stat p.value
## ASV114 1.0000 1.0000 1.000 0.001 ***
## ASV193 1.0000 1.0000 1.000 0.001 ***
## ASV301 1.0000 1.0000 1.000 0.001 ***
## ASV302 1.0000 1.0000 1.000 0.001 ***
## ASV103 0.8571 1.0000 0.926 0.016 *
## ASV65 1.0000 0.8333 0.913 0.015 *
## ASV451 1.0000 0.8333 0.913 0.011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## All have strong indicator stats value > 0.7
tax_table(TxODA)## Taxonomy Table: [133 taxa by 7 taxonomic ranks]:
## Kingdom Phylum Class
## ASV6 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV8 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV10 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV11 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV12 "Bacteria" "Cyanobacteria/Chloroplast" "Chloroplast"
## ASV15 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV20 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV22 "Bacteria" "Firmicutes" "Bacilli"
## ASV26 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV28 "Bacteria" "Cyanobacteria/Chloroplast" "Chloroplast"
## ASV29 "Bacteria" "Firmicutes" "Bacilli"
## ASV30 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV31 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV34 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV38 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV40 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV43 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV44 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV47 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV53 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV54 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV61 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV62 "Bacteria" "Firmicutes" "Bacilli"
## ASV65 "Bacteria" "Cyanobacteria/Chloroplast" "Chloroplast"
## ASV68 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV69 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV70 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV72 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV74 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV76 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV81 "Bacteria" "Firmicutes" "Clostridia"
## ASV87 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV90 "Bacteria" "Cyanobacteria/Chloroplast" "Chloroplast"
## ASV91 "Bacteria" "Firmicutes" "Erysipelotrichia"
## ASV97 "Bacteria" "Firmicutes" "Clostridia"
## ASV100 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV103 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV105 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV107 "Bacteria" "Firmicutes" "Bacilli"
## ASV114 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV121 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV123 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV124 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV132 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV133 "Bacteria" "Chloroflexi" "Thermomicrobia"
## ASV135 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV137 "Bacteria" "Firmicutes" "Erysipelotrichia"
## ASV151 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV158 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV159 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV167 "Bacteria" "Firmicutes" "Bacilli"
## ASV170 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV174 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV178 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV183 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV190 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV192 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV193 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV194 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV196 "Bacteria" "Firmicutes" "Erysipelotrichia"
## ASV199 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV201 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV203 "Bacteria" "Firmicutes" "Clostridia"
## ASV209 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV210 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV215 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV219 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV223 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV232 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV238 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV241 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV242 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV245 "Bacteria" "Firmicutes" "Clostridia"
## ASV248 "Bacteria" "Firmicutes" "Clostridia"
## ASV250 "Bacteria" "Firmicutes" "Clostridia"
## ASV253 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV256 "Bacteria" "Chloroflexi" "Thermomicrobia"
## ASV260 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV264 "Bacteria" "Firmicutes" "Clostridia"
## ASV267 "Bacteria" "Firmicutes" "Clostridia"
## ASV268 "Bacteria" "Firmicutes" "Clostridia"
## ASV278 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV281 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV301 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV302 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV303 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV309 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV319 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV326 "Bacteria" "Firmicutes" "Erysipelotrichia"
## ASV331 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV332 "Bacteria" "Bacteroidetes" "Bacteroidia"
## ASV340 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV343 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV349 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV361 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV375 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV376 "Bacteria" "Proteobacteria" "Betaproteobacteria"
## ASV381 "Bacteria" "Firmicutes" "Clostridia"
## ASV403 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV408 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV451 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV472 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV480 "Bacteria" "Firmicutes" "Clostridia"
## ASV482 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV483 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV489 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV562 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV570 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV591 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV665 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV672 "Bacteria" "Chloroflexi" NA
## ASV728 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV759 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV762 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV784 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV806 "Bacteria" "Verrucomicrobia" "Spartobacteria"
## ASV808 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV819 "Bacteria" "Bacteroidetes" "Bacteroidia"
## ASV832 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV835 "Bacteria" "Verrucomicrobia" "Spartobacteria"
## ASV850 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV851 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV884 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV925 "Bacteria" "Firmicutes" "Clostridia"
## ASV959 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV1107 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV1133 "Bacteria" "Acidobacteria" "Acidobacteria_Gp16"
## ASV1155 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV1156 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV1180 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV1231 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV1470 "Bacteria" "Chloroflexi" "Chloroflexia"
## Others NA NA NA
## Order Family
## ASV6 "Actinomycetales" "Sanguibacteraceae"
## ASV8 "Pseudomonadales" "Pseudomonadaceae"
## ASV10 "Oceanospirillales" "Halomonadaceae"
## ASV11 "Enterobacteriales" "Enterobacteriaceae"
## ASV12 "Chloroplast" "Streptophyta"
## ASV15 "Enterobacteriales" "Enterobacteriaceae"
## ASV20 "Pseudomonadales" "Pseudomonadaceae"
## ASV22 "Lactobacillales" "Enterococcaceae"
## ASV26 "Actinomycetales" "Pseudonocardiaceae"
## ASV28 "Chloroplast" "Streptophyta"
## ASV29 "Lactobacillales" "Carnobacteriaceae"
## ASV30 "Solirubrobacterales" "Patulibacteraceae"
## ASV31 "Actinomycetales" "Propionibacteriaceae"
## ASV34 "Pseudomonadales" "Pseudomonadaceae"
## ASV38 "Pseudomonadales" "Pseudomonadaceae"
## ASV40 "Pseudomonadales" "Moraxellaceae"
## ASV43 "Actinomycetales" "Pseudonocardiaceae"
## ASV44 "Actinomycetales" "Microbacteriaceae"
## ASV47 "Enterobacteriales" "Enterobacteriaceae"
## ASV53 "Pseudomonadales" "Moraxellaceae"
## ASV54 "Coriobacteriales" "Coriobacteriaceae"
## ASV61 "Actinomycetales" NA
## ASV62 "Bacillales" "Staphylococcaceae"
## ASV65 "Chloroplast" "Chlorophyta"
## ASV68 "Actinomycetales" "Mycobacteriaceae"
## ASV69 "Actinomycetales" "Nocardiaceae"
## ASV70 "Actinomycetales" "Pseudonocardiaceae"
## ASV72 "Actinomycetales" "Pseudonocardiaceae"
## ASV74 "Actinomycetales" "Propionibacteriaceae"
## ASV76 "Pseudomonadales" "Pseudomonadaceae"
## ASV81 "Clostridiales" "Ruminococcaceae"
## ASV87 "Enterobacteriales" "Enterobacteriaceae"
## ASV90 "Chloroplast" "Streptophyta"
## ASV91 "Erysipelotrichales" "Erysipelotrichaceae"
## ASV97 "Clostridiales" "Lachnospiraceae"
## ASV100 "Rhizobiales" "Methylobacteriaceae"
## ASV103 "Solirubrobacterales" "Solirubrobacteraceae"
## ASV105 "Actinomycetales" "Pseudonocardiaceae"
## ASV107 "Lactobacillales" "Carnobacteriaceae"
## ASV114 "Solirubrobacterales" "Solirubrobacteraceae"
## ASV121 "Actinomycetales" "Microbacteriaceae"
## ASV123 "Enterobacteriales" "Enterobacteriaceae"
## ASV124 "Actinomycetales" "Mycobacteriaceae"
## ASV132 "Actinomycetales" "Microbacteriaceae"
## ASV133 "Sphaerobacterales" "Sphaerobacteraceae"
## ASV135 "Solirubrobacterales" "Solirubrobacteraceae"
## ASV137 "Erysipelotrichales" "Erysipelotrichaceae"
## ASV151 "Actinomycetales" "Mycobacteriaceae"
## ASV158 "Actinomycetales" "Nocardioidaceae"
## ASV159 "Actinomycetales" "Microbacteriaceae"
## ASV167 "Bacillales" "Bacillaceae_1"
## ASV170 "Actinomycetales" "Microbacteriaceae"
## ASV174 "Actinomycetales" "Microbacteriaceae"
## ASV178 "Actinomycetales" "Intrasporangiaceae"
## ASV183 "Actinomycetales" "Nocardioidaceae"
## ASV190 "Actinomycetales" "Microbacteriaceae"
## ASV192 "Rubrobacterales" "Rubrobacteraceae"
## ASV193 "Rubrobacterales" "Rubrobacteraceae"
## ASV194 "Actinomycetales" "Mycobacteriaceae"
## ASV196 "Erysipelotrichales" "Erysipelotrichaceae"
## ASV199 "Rhizobiales" "Methylobacteriaceae"
## ASV201 "Actinomycetales" "Nocardioidaceae"
## ASV203 "Clostridiales" "Lachnospiraceae"
## ASV209 "Actinomycetales" "Nocardioidaceae"
## ASV210 "Solirubrobacterales" "Solirubrobacteraceae"
## ASV215 "Actinomycetales" "Propionibacteriaceae"
## ASV219 "Rhizobiales" "Methylobacteriaceae"
## ASV223 "Rhizobiales" "Bradyrhizobiaceae"
## ASV232 "Actinomycetales" "Nocardioidaceae"
## ASV238 "Actinomycetales" "Nocardioidaceae"
## ASV241 "Rhizobiales" "Methylobacteriaceae"
## ASV242 "Enterobacteriales" "Enterobacteriaceae"
## ASV245 "Clostridiales" "Ruminococcaceae"
## ASV248 "Clostridiales" "Eubacteriaceae"
## ASV250 "Clostridiales" "Lachnospiraceae"
## ASV253 "Gaiellales" "Gaiellaceae"
## ASV256 "Sphaerobacterales" "Sphaerobacteraceae"
## ASV260 "Actinomycetales" "Pseudonocardiaceae"
## ASV264 "Clostridiales" "Ruminococcaceae"
## ASV267 "Clostridiales" "Lachnospiraceae"
## ASV268 "Clostridiales" "Lachnospiraceae"
## ASV278 "Actinomycetales" "Propionibacteriaceae"
## ASV281 "Actinomycetales" NA
## ASV301 "Alphaproteobacteria_incertae_sedis" "Geminicoccus"
## ASV302 "Rhizobiales" "Rhodobiaceae"
## ASV303 "Actinomycetales" "Streptomycetaceae"
## ASV309 NA NA
## ASV319 "Rhizobiales" "Methylobacteriaceae"
## ASV326 "Erysipelotrichales" "Erysipelotrichaceae"
## ASV331 "Solirubrobacterales" NA
## ASV332 "Bacteroidales" "Bacteroidaceae"
## ASV340 "Actinomycetales" "Pseudonocardiaceae"
## ASV343 "Actinomycetales" "Mycobacteriaceae"
## ASV349 "Actinomycetales" "Cellulomonadaceae"
## ASV361 "Actinomycetales" "Pseudonocardiaceae"
## ASV375 "Solirubrobacterales" "Conexibacteraceae"
## ASV376 "Burkholderiales" "Alcaligenaceae"
## ASV381 "Clostridiales" "Ruminococcaceae"
## ASV403 "Rhizobiales" "Methylobacteriaceae"
## ASV408 "Actinomycetales" NA
## ASV451 "Rhizobiales" "Methylobacteriaceae"
## ASV472 "Actinomycetales" "Kineosporiaceae"
## ASV480 "Clostridiales" "Ruminococcaceae"
## ASV482 "Alphaproteobacteria_incertae_sedis" "Geminicoccus"
## ASV483 "Actinomycetales" "Micrococcaceae"
## ASV489 "Rhodospirillales" "Reyranella"
## ASV562 "Rhizobiales" "Hyphomicrobiaceae"
## ASV570 "Rhizobiales" "Methylobacteriaceae"
## ASV591 "Solirubrobacterales" "Solirubrobacteraceae"
## ASV665 "Sphingomonadales" "Sphingomonadaceae"
## ASV672 NA NA
## ASV728 "Acidimicrobiales" "Acidimicrobiaceae"
## ASV759 "Actinomycetales" "Propionibacteriaceae"
## ASV762 "Acidimicrobiales" "Acidimicrobiaceae"
## ASV784 "Actinomycetales" "Intrasporangiaceae"
## ASV806 "Terrimicrobium" NA
## ASV808 "Gaiellales" "Gaiellaceae"
## ASV819 "Bacteroidales" "Bacteroidaceae"
## ASV832 "Alphaproteobacteria_incertae_sedis" "Geminicoccus"
## ASV835 "Terrimicrobium" NA
## ASV850 "Rhizobiales" "Hyphomicrobiaceae"
## ASV851 "Alphaproteobacteria_incertae_sedis" "Geminicoccus"
## ASV884 "Solirubrobacterales" NA
## ASV925 "Clostridiales" "Lachnospiraceae"
## ASV959 "Alphaproteobacteria_incertae_sedis" "Geminicoccus"
## ASV1107 "Solirubrobacterales" NA
## ASV1133 "Gp16" NA
## ASV1155 "Rhizobiales" "Xanthobacteraceae"
## ASV1156 "Solirubrobacterales" "Solirubrobacteraceae"
## ASV1180 "Actinomycetales" "Nocardioidaceae"
## ASV1231 "Rubrobacterales" "Rubrobacteraceae"
## ASV1470 "Kallotenuales" "Kallotenuaceae"
## Others NA NA
## Genus Species
## ASV6 "Sanguibacter" NA
## ASV8 "Pseudomonas" NA
## ASV10 "Salinicola" "socius"
## ASV11 "Buttiauxella" NA
## ASV12 NA NA
## ASV15 "Enterobacter" NA
## ASV20 "Pseudomonas" NA
## ASV22 "Enterococcus" NA
## ASV26 "Actinomycetospora" NA
## ASV28 NA NA
## ASV29 "Catellicoccus" NA
## ASV30 "Patulibacter" "minatonensis"
## ASV31 "Friedmanniella" NA
## ASV34 "Pseudomonas" NA
## ASV38 "Pseudomonas" NA
## ASV40 "Acinetobacter" NA
## ASV43 "Pseudonocardia" "endophytica"
## ASV44 "Microbacterium" NA
## ASV47 "Pantoea" NA
## ASV53 "Acinetobacter" NA
## ASV54 "Denitrobacterium" NA
## ASV61 NA NA
## ASV62 "Staphylococcus" NA
## ASV65 NA NA
## ASV68 "Mycobacterium" NA
## ASV69 "Rhodococcus" NA
## ASV70 "Actinomycetospora" "chiangmaiensis"
## ASV72 "Actinomycetospora" NA
## ASV74 NA NA
## ASV76 "Pseudomonas" NA
## ASV81 "Intestinimonas" NA
## ASV87 "Pantoea" NA
## ASV90 NA NA
## ASV91 "Clostridium_XVIII" NA
## ASV97 NA NA
## ASV100 "Methylobacterium" NA
## ASV103 "Solirubrobacter" NA
## ASV105 "Actinomycetospora" NA
## ASV107 "Carnobacterium" NA
## ASV114 "Solirubrobacter" NA
## ASV121 "Agrococcus" NA
## ASV123 NA NA
## ASV124 "Mycobacterium" NA
## ASV132 "Curtobacterium" NA
## ASV133 NA NA
## ASV135 "Solirubrobacter" NA
## ASV137 "Clostridium_XVIII" NA
## ASV151 "Mycobacterium" "JC2972"
## ASV158 "Nocardioides" NA
## ASV159 "Microbacterium" NA
## ASV167 "Bacillus" NA
## ASV170 "Curtobacterium" NA
## ASV174 "Microbacterium" NA
## ASV178 NA NA
## ASV183 "Nocardioides" NA
## ASV190 "Pseudoclavibacter" "helvolus"
## ASV192 "Rubrobacter" NA
## ASV193 "Rubrobacter" NA
## ASV194 "Mycobacterium" NA
## ASV196 "Clostridium_XVIII" NA
## ASV199 NA NA
## ASV201 "Nocardioides" NA
## ASV203 NA NA
## ASV209 "Nocardioides" "ginsengagri"
## ASV210 "Solirubrobacter" NA
## ASV215 "Microlunatus" NA
## ASV219 NA NA
## ASV223 "Bradyrhizobium" NA
## ASV232 "Nocardioides" NA
## ASV238 "Nocardioides" NA
## ASV241 "Methylobacterium" NA
## ASV242 "Pantoea" NA
## ASV245 "Intestinimonas" NA
## ASV248 "Anaerofustis" NA
## ASV250 NA NA
## ASV253 "Gaiella" NA
## ASV256 "Sphaerobacter" NA
## ASV260 "Pseudonocardia" NA
## ASV264 NA NA
## ASV267 "Clostridium_XlVa" NA
## ASV268 NA NA
## ASV278 "Microlunatus" NA
## ASV281 NA NA
## ASV301 NA NA
## ASV302 NA NA
## ASV303 "Streptomyces" NA
## ASV309 NA NA
## ASV319 "Microvirga" "zambiensis"
## ASV326 "Clostridium_XVIII" NA
## ASV331 NA NA
## ASV332 "Bacteroides" NA
## ASV340 "Pseudonocardia" NA
## ASV343 "Mycobacterium" NA
## ASV349 "Cellulomonas" NA
## ASV361 "Pseudonocardia" NA
## ASV375 "Conexibacter" NA
## ASV376 "Achromobacter" NA
## ASV381 "Anaerofilum" NA
## ASV403 "Microvirga" NA
## ASV408 NA NA
## ASV451 "Microvirga" NA
## ASV472 "Pseudokineococcus" NA
## ASV480 "Clostridium_IV" NA
## ASV482 NA NA
## ASV483 "Arthrobacter" NA
## ASV489 NA NA
## ASV562 "Rhodoplanes" NA
## ASV570 "Microvirga" "lotononidis"
## ASV591 "Solirubrobacter" NA
## ASV665 "Sphingopyxis" NA
## ASV672 NA NA
## ASV728 "Ilumatobacter" NA
## ASV759 "Friedmanniella" NA
## ASV762 "Ilumatobacter" NA
## ASV784 "Janibacter" NA
## ASV806 NA NA
## ASV808 "Gaiella" NA
## ASV819 "Bacteroides" NA
## ASV832 NA NA
## ASV835 NA NA
## ASV850 "Pedomicrobium" NA
## ASV851 NA NA
## ASV884 NA NA
## ASV925 "Clostridium_XlVa" NA
## ASV959 NA NA
## ASV1107 NA NA
## ASV1133 NA NA
## ASV1155 NA NA
## ASV1156 "Solirubrobacter" NA
## ASV1180 "Nocardioides" NA
## ASV1231 "Rubrobacter" NA
## ASV1470 "Kallotenue" NA
## Others NA NA## Need an OTU ID in the taxonomy table to match to
tax_table(TxODA) <- cbind(tax_table(TxODA), ASV=taxa_names(TxODA))
head(tax_table(TxODA))## Taxonomy Table: [6 taxa by 8 taxonomic ranks]:
## Kingdom Phylum Class
## ASV6 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV8 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV10 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV11 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV12 "Bacteria" "Cyanobacteria/Chloroplast" "Chloroplast"
## ASV15 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## Order Family Genus Species
## ASV6 "Actinomycetales" "Sanguibacteraceae" "Sanguibacter" NA
## ASV8 "Pseudomonadales" "Pseudomonadaceae" "Pseudomonas" NA
## ASV10 "Oceanospirillales" "Halomonadaceae" "Salinicola" "socius"
## ASV11 "Enterobacteriales" "Enterobacteriaceae" "Buttiauxella" NA
## ASV12 "Chloroplast" "Streptophyta" NA NA
## ASV15 "Enterobacteriales" "Enterobacteriaceae" "Enterobacter" NA
## ASV
## ASV6 "ASV6"
## ASV8 "ASV8"
## ASV10 "ASV10"
## ASV11 "ASV11"
## ASV12 "ASV12"
## ASV15 "ASV15"Select_my_sigASVs <- subset_taxa(TxODA, ASV %in% my_sigASVs)
tax_table(Select_my_sigASVs)## Taxonomy Table: [5 taxa by 8 taxonomic ranks]:
## Kingdom Phylum Class
## ASV20 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## ASV114 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV193 "Bacteria" "Actinobacteria" "Actinobacteria"
## ASV301 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## ASV302 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## Order Family
## ASV20 "Pseudomonadales" "Pseudomonadaceae"
## ASV114 "Solirubrobacterales" "Solirubrobacteraceae"
## ASV193 "Rubrobacterales" "Rubrobacteraceae"
## ASV301 "Alphaproteobacteria_incertae_sedis" "Geminicoccus"
## ASV302 "Rhizobiales" "Rhodobiaceae"
## Genus Species ASV
## ASV20 "Pseudomonas" NA "ASV20"
## ASV114 "Solirubrobacter" NA "ASV114"
## ASV193 "Rubrobacter" NA "ASV193"
## ASV301 NA NA "ASV301"
## ASV302 NA NA "ASV302"ASV20 <- subset_taxa(TxODA, ASV == "ASV20")
plot_bar(ASV20, "Type", "Abundance")## Warning in psmelt(physeq): The sample variables:
## Species
## have been renamed to:
## sample_Species
## to avoid conflicts with taxonomic rank names.
## Looks like found in 6 samples of cloaca (each stack is a sample), and none in fecal
ASV301 <- subset_taxa(TxODA, ASV == "ASV301")
plot_bar(ASV301, "Type", "Abundance")## Warning in psmelt(physeq): The sample variables:
## Species
## have been renamed to:
## sample_Species
## to avoid conflicts with taxonomic rank names.
## This is the reverse, 6 samples fecal and none in cloacal. Family Geminicoccus is this ASV.
##### THE END (for now ;)) #######